management information system sri chandrasekharendra

Management Information System

Subject Code: CS7E3

Dr.R.Poorvadevi, Assistant Professor, CSE

Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya

Department of Computer Science and Engineering

Course Material Prepared by Dr.R.Poorvadevi, AP/CSE,SCSVMV

Course Plan on

Decision Making Process [DMP]

Prepared By,

Dr.R.Poorvadevi,

AP / CSE, SCSVMV


Agenda• Definition • Need for Decision Making process• Types of Decision Making Models• Stages in Decision Making process• Model Comparison• Typical Input Output • Example• Summary• Conclusion• Question and Analysis• References


Definition

Decision-making is a cognitive process that results in the selection of a course of action among several alternative scenarios. Therefore, corporate decision-making is the most critical process in any organization.

** George Terry, Defines the decision making “as the selection of one behavior alternative from two or more possible alternatives”.


What is Decision Making?

Decision making is the developing concepts leading to the selection of a course of action among variations.


Decision Making


Need for Decision Making Process

• The following questions one should ask when it comes to identifying the purpose of the decision.

• What exactly is the problem?• Why the problem should be solved?• Who are the affected parties of the problem?• Does the problem have a deadline or a specific

time-line?


Context of MIS and Decision Making Process


Types of Decision

■ Unstructured/ Non-programmed

■ Structured/ Programmed

■ Semi-structured


Information Requirements of Key Decision-Making Groups in a Firm


Phases of Decision Making Process

� Intelligence

� Design

� Choice

� Implementation


Stages in Decision making


Phases of Decision Making Process

■ Intelligence gathering– Definition of problem– Data gathered on scope– Constraints identified

■ Design phase– Alternatives identified and assessed

■ Choice– Selection of an alternative

■ Implementation– Testing the selected alternative.


Typical Inputs and Outputs

■ Inputs: Information from the TPS■ Outputs: hard and softcopy reports

– Scheduled reports– On-demand reports– Key-indicator (business fundamentals)– Exception reports


A Simple example for DMP


Process and Modeling in Decision-Making

• There are two basic models in decision-making −• Rational models• Normative model• The rational models are based on cognitive

judgments and help in selecting the most logical and sensible alternative. Examples of such models include - decision matrix analysis, Pugh matrix, SWOT analysis, Pareto analysis and decision trees, selection matrix, etc.


Rational ModelA rational decision making model takes the

following steps −• Identifying the problem,• Identifying the important criteria for the process

and the result,• Considering all possible solutions,• Calculating the consequences of all solutions and

comparing the probability of satisfying the criteria,

• Selecting the best option.


Normative Model

The normative model of decision-making considers constraints that may arise in making decisions, such as time, complexity, uncertainty, and inadequacy of resources.


Normative Decision Model

Normative decision-making is characterized by −• Limited information processing - A person can

manage only a limited amount of information.• Judgmental heuristics - A person may use

shortcuts to simplify the decision making process.

• Satisfying - A person may choose a solution that is just "good enough".


Dynamic decision-making (DDM)• Dynamic Decision-Making is synergetic

decision-making involving interdependent systems, in an environment that changes over time either due to the previous actions of the decision-maker or due to events that are outside of the control of the decision-maker.

• These decision-makings are more complex and real-time.

• Dynamic decision-making involves observing how people used their experience to control the system's dynamics and noting down the best decisions taken thereon.


Static Vs Dynamic ModelsStatic Model Dynamic ModelShow the value of various attributes in a balanced system.

Consider the change in data values over time.

Work best in static systems. Consider effect of system behavior over time.

Do not take into consideration the time-based variances

Re-calculate equations as time changes.

Do not work well in real-time systems Can be applied only in dynamic systems

Involve less data. Involves More data

Easy to analyze and Produce faster results. Easy to Analyze.


Sensitivity Analysis

• Sensitivity analysis is a technique used for distributing the uncertainty in the output of a mathematical model or a system to different sources of uncertainty in its inputs.

• From business decision perspective, the sensitivity analysis helps an analyst to identify cost drivers as well as other quantities to make an informed decision.


Simulation Techniques

• Simulation is a technique that imitates the operation of a real-world process or system over time.

• Simulation techniques can be used to assist management decision making, where analytical methods are either not available or cannot be applied.

• Some of the typical business problem areas where simulation techniques are used are −

• Inventory control• Queuing problem• Production planning


Operations Research Techniques

• Operational Research (OR) includes a wide range of problem-solving techniques involving various advanced analytical models and methods applied. It helps in efficient and improved decision-making.


Heuristic Programming

• Heuristic programming refers to a branch of artificial intelligence. It consists of programs that are self-learning in nature.

• However, these programs are not optimal in nature, as they are experience-based techniques for problem solving.

• Most basic heuristic programs would be based on pure 'trial-error' methods.


Group Decision Support-Systems(GDSS)

■ Very interesting field■ How can information technology improve how

decisions are made by groups?■ Interactive, computer-based systems that

facilitates solving of unstructured problems by a set of decision makers

■ Used in conference rooms with special hardware and software

■ Support increased meeting sizes with increased productivity


GDSS■ Common characteristics

– Meeting moderation/facilitation– Signed and anonymous comments– Structured deliberations

• Presentation period• Comment period• Automated collation of comments• “Voting”

■ Face-to-face and remote


Applications of GDSS

■ Applications– Where time is critical– Where participants are geographically dispersed– Where authority obstructs communication– Military– Business– Government


Intelligent systems for Decision Support Systems

• Artificial intelligence (AI)

• Expert systems

• Case-based reasoning

• Intelligent agents


Intelligent Agents in P&G’s Supply Chain Network

Intelligent agentsarehelping Procter & Gamble shortenthe

replenishment cycles for products, such as a box of Tide.Course Material Prepared by Dr.R.Poorvadevi, AP/CSE,SCSVMV

Management Information Systems (MIS)

Systems for Decision Support

• Help managers monitor and control a business by providing information on the firm’s performance.

• Typically produce fixed, regularly scheduled reports based on data from TPS.

• E.g., summary of monthly or annual sales for each of the major sales territories of a company.

• Exception reports: highlighting exceptional conditions only.


Decision-Support Systems (DSS)

• Support semistructured and unstructured problem analysis.

• Earliest DSS were model-driven:

• “What-if” analysis: model is developed, various input factors are changed, and the output changes are measured.

• Data-driven DSS:

• Use OLAP and data mining to analyze large pools of data in major corporate systems.



Systems for Decision SupportInteractive Session: People

Data-Driven Schools• Read the Interactive Session and then discuss the following

questions:

• Identify and describe the problem discussed in this case.

• How do data-driven DSS provide a solution to this problem? What are the inputs and outputs of these systems?

• What people, organization, and technology issues must be addressed by this solution?

• How successful is this solution? Explain your answer.

• Should all school districts use such a data-driven approach to education? Why or why not?


Components of DSS


• DSS database: collection of current or historical data from a number of applications or groups

• DSS software system

• Software tools that are used for data analysis

• OLAP tools

• Data mining tools

• Mathematical and analytical models

• DSS user interface


Decision Making and Information Systems

The main components of the DSS are the DSS database, the DSS software system, and the user interface. The DSS database may be a small database residing on a PC or a large data warehouse.

Overview of a Decision-Support System


Systems for Decision Support• Models: abstract representation that illustrates the components or

relationships of a phenomenon

• Statistical modeling helps establish relationships.

• E.g., relating product sales to differences in age, income, or other factors

• Optimization models, forecasting models

• Sensitivity analysis models

• Ask “what-if” questions repeatedly to determine the impact on outcomes of changes in one or more factors.

• E.g., what happens if we raise product price by 5 percent



This table displays the results of a sensitivity analysis of the effect of changing the sales price of a necktie and the cost per unit on the product’s break-even point. It answers the question, “What happens to the break-even point if the sales price and the cost to make each unit increase or decrease?”

Sensitivity Analysis


Using Spreadsheet Tables to Support Decision Making


• Spreadsheet tables can answer multiple dimensions of questions.

• Time of day and average purchase• Payment type and average purchase• Payment type, region, and source

• Pivot table• Displays two or more dimensions of data in a convenient format



This list shows a portion of the order transactions for Online Management Training Inc. on October 28, 2009.

Sample List of Transactions for Online Management Training Inc. on October 28, 2009



This pivot table was created using Excel 2007 to quickly produce a table showing the relationship between region and number of customers.

A Pivot Table That Examines the Regional Distribution of Customers



In this pivot table, we can examine where customers come from in terms of region and advertising source. It appears nearly 30 percent of the customers respond to e-mail campaigns, and there are some regional variations.

A Pivot Table That Examines Customer Regional Distribution and Advertising Source


Data Visualization and Geographic Information Systems (GIS)


• Data visualization tools:

• Present data in graphical form to help users see patterns and relationships in large quantities of data.

• Geographic information systems (GIS):

• Use data visualization technology to analyze and display data in the form of digitized maps.

• Support decisions that require knowledge about the geographic distribution of people or other resources.

SmartMonet.com Map the MarketCourse Material Prepared by

Dr.R.Poorvadevi, AP/CSE,SCSVMV

SmartMoney.com Map the Market

The map is a powerful new tool for spotting investment trends and opportunities. This quick-start guide will show you how to interpret the map, drill down for detailed information and customize your view. For advanced tips, see our article, Secrets to Using the Map of the Market. The market at a glanceThe map lets you watch more than 500 stocks at once, with data updated every 15 minutes. Each colored rectangle in the map represents an individual company. The rectangle's size reflects the company's market cap and the color shows price performance. (Green means the stock price is up; red means it's down. Dark colors are neutral). Move the mouse over a company rectangle and a little panel will pop up with more information. For example, the picture at left shows a group of technology companies. The mouse is pointing to a dark rectangle, representing Oracle. Notice that the green rectangle at the upper left is much bigger than the others. Exactly which Redmond-based software behemoth it represents is left as an exercise for the reader.



Somerset County, NewJersey, developed a GISbased on ESRI softwareto provide Web accessto geospatial data aboutflood conditions. The system provides information that helps emergency responders and county residents prepare for floods and enables emergency managers to make decisions more quickly.


Web-Based Customer Decision-Support Systems (CDSS)


• Support customers in the decision-making process,

• Include: search engines, intelligent agents, online catalogs, Web directories, newsgroups, e-mail, and so on

• Many firms have customer Web sites where all the information, models, or other analytical tools for evaluating alternatives are concentrated in one location.

• E.g., T. Rowe Price online tools, guides for college planning, retirement planning, estate planning, and so on

Realtor.com


Executive Support Systems (ESS)

• Bring together data from many different internal and external sources, often through a portal.

• Digital dashboard: gives senior executives a picture of the overall performance of an organization.

• Drill down capability: enables an executive to zoom in on details or zoom out for a broader view.

• Used to monitor organizational performance, track activities of competitors, identify changing market conditions, spot problems, identify opportunities, and forecast trends.



Group Decision-Support Systems (GDSS)

• Interactive, computer-based systems that facilitate solving of unstructured problems by set of decision makers.

• Used in conference rooms with special hardware and software for collecting, ranking, storing ideas and decisions.

• Promote a collaborative atmosphere by guaranteeing contributors’ anonymity.

• Support increased meeting sizes with increased productivity.



• Intelligent techniques for enhancing decision making• Many based on artificial intelligence (AI)

• Computer-based systems (hardware and software) that attempt to emulate human behavior and thought patterns

• Include:• Expert systems• Case-based reasoning• Fuzzy logic• Neural networks• Genetic algorithms• Intelligent agents

Intelligent Systems for Decision Support


• Expert systems• Model human knowledge as a set of rules that are

collectively called the knowledge base

• 200 to 10,000 rules, depending on complexity

• The system’s inference engine searches through the rules and “fires” those rules that are triggered by facts gathered and entered by the user.

• Useful for dealing with problems of classification in which there are relatively few alternative outcomes and in which these possible outcomes are all known in advance



An expert system contains a set of rules to be followed when used. The rules are interconnected; the number of outcomes is known in advance and is limited; there are multiple paths to the same outcome; and the system can consider multiple rules at a single time. The rules illustrated are for a simple credit-granting expert system.

Rules in an Expert System



The membership functions for the input called temperature are in the logic of the thermostat to control the room temperature. Membership functions help translate linguistic expressions, such as warm, into numbers that the computer can manipulate

Intelligent Systems for Decision SupportFuzzy Logic for Temperature Control


A neural network uses rules it “learns” from patterns in data to construct a hidden layer of logic. The hidden layer then processes inputs, classifying them based on the experience of the model. In this example, the neural network has been trained to distinguish between valid and fraudulent credit card purchases.

Intelligent Systems for Decision SupportHow a Neural Network Works


This example illustrates an initial population of “chromosomes,” each representing a different solution. The genetic algorithm uses an iterative process to refine the initial solutions so that the better ones, those with the higher fitness, are more likely to emerge as the best solution.

Intelligent Systems for Decision SupportThe Components of a Genetic Algorithm


Game Thoery based DM

• Game theory is a key element in most decision making processes involving two or more people or organizations.

• game theory can predict the outcome of complex decision making processes, and how it can help to improve negotiation and decision-making skills.

• It is grounded in well-established theory, yet the wide-ranging international examples used to illustrate its application offer a fresh approach to what is becoming an essential weapon in the armory of the informed manager.


Tree based Decision making

• A decision tree is a map of the possible outcomes of a series of related choices. It allows an individual or organization to weigh possible actions against one another based on their costs, probabilities, and benefits.

• As the name goes, it uses a tree-like model of decisions. They can be used either to drive informal discussion or to map out an algorithm that predicts the best choice mathematically.

• A decision tree typically starts with a single node, which branches into possible outcomes. Each of those outcomes leads to additional nodes, which branch off into other possibilities. This gives it a tree-like shape.


Output: A Decision Tree for “buys_computer”


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student? credit rating?

<=30 >40

no yes yes

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31..40

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fairexcellentyesno

Pay off Matrices• Payoff Matrix & Decision Rule. Payoff Matrix. The Payoff Matrix is an

expression of the First Law of Decision Science. Each row represents one action that the decision maker might or might not freely choose to perform; Each column represents a possible state of nature.

• Payoff matrices are important tools in risk analysis and decision making that are used to identify risk in both everyday and multibillion-dollar business decisions. The payoff matrix method breaks the decision process down into decision alternatives and states of nature.

• A profit table (payoff table) can be a useful way to represent and analyse a scenario where there is a range of possible outcomes and a variety of possible responses. A payoff table simply illustrates all possible profits/losses and as such is often used in decison making under uncertainty.


Payoff Matrices• Payoff matrices are important tools in risk analysis and decision

making that are used to identify risk in both everyday and multibillion-dollar business decisions. The payoff matrix method breaks the decision process down into decision alternatives and states of nature. When making important decisions in risk management in engineering, the expected monetary value (EMV) method is recommended whenever possible.

• Overall, the payoff matrix method is great for any type of risk taker. Risk takers, risk avoiders, and anyone in between can use this technique to come up with an answer that suits their needs.

• Each method can also be used with one of the other methods to help ease the decision maker. A decision is obvious if it has the same outcome as maximin, maximax, minimax regret, and EMV.


Summary

• Definition of Decision making• Need for Decision making• 7 important steps related to decision making• Two Types of Decision – Normative and Rational• Static Vs Dynamic Decision making model• Phases of Decision making• Group Decision Support Systems (GDSS)• Applications of Decision making process


Conclusion

The above Lecture covered the topics which includes,

Decision making definition, Types of Decision making models and various steps involved in the decision making process.

Finally the lecture also covers the application of decision making techniques in various real-time decision making process.


References1. Alvani, S.M. (2012). Public Management (Edition 23) Tehran: Nashodani. 2. Ajayi, I. A. and Omirin F. F. (2007): The use of Management Information

Systems (MIS) in Decisionmaking in South-West Nigerian Universities. 3. Asefeh Asemi, Ali Safari, Adeleh Asemi Zavareh, the Role of Management

Information System (MIS) and Decision Support System (DSS) for Manager’s Decision Making Process, International Journal of Business and Management Vol. 6, No. 7; July 2011.

4. Barton J., Parolin B., Weiley V., A Spatial Decision Support System for the Management Of Public Housing, in Recent Advances in Design and Decision Support Systems in Architecture and Urban Planning, Springer Science and Business Media, 2005, p. 69&84

5. Bendoly E., Excel Basics to Blackbelt. An Accelerated Guide to Decision Support Designs, Cambridge University Press 2008

6. Bresfelean V.P., Ghisoiu N., Lacurezeanu R., Sitar& Taut D.&A., Towards the Development of Decision Support in Academic Environments, Proceedings of ITI 2009, Cavtat, Croatia, 2009, p. 343&348.

7. C.W. Holsapple, A.B.Whinston, Decision support systems: A knowledge-based approach, St. Paul: West Publishing, 2006. Course Material Prepared by


References8. Davis, Timothy; Geist, Robert; Matzko, Sarah; Westall, James (2004) a First

Step". Technical Symposium on Computer Science Education: 125–129.

9. Feizi, K& Moghadassi, A. (2012). Application of Decision Support Systems (45) Management Studies in Management Decision.

10. Gabriel, J. M. O. (2013): The Systems Concept: An unpublished Lecture note giving to B.sc

11. Year 3 Students of Faculty of Management Sciences, Rivers State University of Science and Technology, Port Harcourt.

12. George, J. M. and Jones G. R. (1996): Understanding and managing Organizational behavior. 1st ed., Addison- Wesley Publishing Company Inc. USA.

13. http://home.ubalt.edu/ntsbarsh/opre640a/partix.htmCourse Material Prepared by


Question & Analysis• What is the Need for Decision making process in Business

operations?• What are the core components of Decision making process?• Compare Normative and Rational decision models?• Classify various types of Dynamic decision making models.• List out various applications of decision making process in

real-time operations.• What is the Role of Decision making process in Artificial

Intelligence(AI)?• How current decision making process differ from the

Traditional decision making process?


Thank you


management information system sri chandrasekharendra

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