ADISSERTATION REPORT ONSTUDY OF SIMULATION MODELLING FORDECISION MAKING

(IN PARTIAL FULFILLMENT OF MASTERS DEGREE IN BUSINESS ADMINISTRATION)

BYMS. CHAITALI SANJEEV GHODKE(IT)

UNDER THE GUIDANCE OFPROF. POONAM RAWAT

M.A.E.E.RSMAHARASHTRA INSTITUTE OF TECHNOLOGYMBA DEPARTMENT (DMSR)KOTHRUD, PUNE 4110382014-2015ACKNOWLEDGEMENTOn the very outset of this Dissertation report, I would like to extend my sincere & heartfelt obligation towards all the personages who have helped me in this endeavour. Without their active guidance, help, cooperation & encouragement, I would not have made headway in the dissertation.I firstly owe my thanks & gratitude to our Principal, DR. L.K.KSHIRSAGAR without whose support my project would not have been successful.I am ineffably indebted to DR. MAHESH ABALE for conscientious guidance and encouragement to accomplish this assignment. I am extremely thankful and pay my gratitude to my faculty guide PROF. POONAM RAWAT for her valuable guidance and support on completion of this project in its presently.I also acknowledge with a deep sense of reverence, my gratitude towards my parents and member of my family, who has always supported me morally as well as economically.Any omission in this brief acknowledgement does not mean lack of gratitude.

Thanking YouChaitali Ghodke

DECLARATIONThis Dissertation is a presentation of my original research work. Wherever contributions of others are involved, every effort is made to indicate this clearly, with due reference to the literature, and acknowledgement of collaborative research and discussions. The work was done under the guidance of Professor Poonam Rawat, at the MAEERs Maharashtra Institute of Technology, MBA Department.

Date: Chaitali S GhodkePlace: PUNE

INDEX

Sr. No.TopicPage no.

1. Introduction1-19

2. Literature Review20-22

3. Research Methodology23-27

a) Topic of Research23

b) Objectives23

c) Significance of Study23

d) Scope of Research24

f)Data Collection24

4.Data Analysis 28-41

5.Research Findings, Recommendations & Conclusion42-47

6.Bibliography48-50

TABLE OF FIGURES

Figure NoTopicPage No

1Components of Decision-Making4

2Flowchart depicting Decision-Making process7

3Levels of decision making11

4Role of Information Systems12

5Classification of applications of information systems13

6Organisational usage of Information System15

7Simulation Modelling Process37

CHAPTER 1 INTRODUCTION

INTRODUCTION: -IT INDUSTRY OVERVIEW:-The information technology (IT) field is a segment of engineering focused on developing, installing, and implementing computer systems and applications to store, process, and receive data electronically. IT can be divided into three broad categories: hardware, software, and the Internet. Hardware refers to the physical equipment of a computer, such as motherboards, memory chips, and microprocessors. Software includes the programs that tell the hardware exactly what to do and how to do it. The Internet is composed of numerous global networks that are connected to each other.Recent trends that have impacted the field of information technology include downsizing of computer systems and replacing big mainframe computers with client-server architecture that allows users greater computing flexibility and increased access to data; and the rapid growth of the Internet and World Wide Web, which has revolutionized information sharing through real-time video conferencing, e-mail services, online research, help lines, and long-distance telephone calls. Internet use on handheld and tablet devices, such as iPhones, iPads, Kindles, and Nooks, through wireless networks has revolutionized people's access to technology.Computer manufacturers and software companies hire a wide range of professionals with many employers located in certain areas, like Northern California, Seattle, and parts of the East Coast. IT employment opportunities vary by industry segment. Within the hardware and software branches of the computer industry, many positions overlap and not every company will hire people to fill positions in each basic segment: design, programming, administration, sales, and service.Jobs in the design and programming segments include designers who research and evaluate the market or existing technology to find opportunities for improvements or new product design. Programmers write the coded instructions that make computers work properly; systems programmers write the instructions that make different computers and peripherals work together; and software programmers write instructions for how computers should respond to various input and what on-screen displays should be generated. Positions in administration and sales include computer administrators who are in charge of daily operations of different kinds of computer systems; network administrators, who attempt to isolate the causes of problems and fix them if a network server goes down; and sales representatives, who work for computer manufactures to market and advertise their products. Sales representatives may also work in retail stores selling products directly to consumers. Computer service is a broad category of careers with positions that include systems setup specialists, technical support specialists, and computer repairers.The U.S. Department of Labor reports that as of May 2012 there were approximately 3.5 million people employed in computer occupations, of which about 1.4 million people worked in jobs related to software development and programming. Information technology weathered the recession of 2007 to 2009 better than many other industries, shedding only 1 percent of its workforce in 2009 and then growing to surpass its 2008 employment numbers by 2010. Employment opportunities for computer professionals, including software engineers, systems administrators, network administrators, computer systems analysts, database administrators, and support specialists are expected to increase through 2022, according to the U.S. Bureau of Labor Statistics.To succeed in this field, computer professionals need flexibility, a formal education, must keep up with the latest technology, and need a solid understanding of computer basics. However, the technology of today may be obsolete in months, if not weeks, and only those individuals who work to remain on the cutting edge will have long-term growth potential during their career.CONCEPTUAL BACKGROUND:-INTRODUCTION TO DECISION MAKING & ITS PROCESS:- Transition from industrial society to information and knowledge society has its impact on social, economic and cultural aspect of life. There are only few aspects of life now- a-days which are unaffected by information technology. In recent years, information systems technology have become crucial and is playing a critical role in contemporary society and dramatically is changing economy and business. Business is conducted in a global environment and simply could not serve without computer based information systems. Furthermore, we are entering the information age because of information technology and information systems usage. The use of information systems especially is often understood to be changing the way business and organisations work as well as help man- agers reduce uncertainty in decision making.

Lucey (2005) emphasises the decision focus of his definition of information systems. He observed that information systems is a system to convert data from internal and external sources into information and to communicate that information in an appropriate form to managers at all levels in all functions to enable them to make timely and effective decisions for planning and controlling the activities for which they are responsible. Decision making is often seen as the centre of what managers do, something that engages most of managers time. In order to take decisions, managers need the right information to serve a wider range of needs. In fact, information has long regarded as a very important aspect of decision making in the business environment because in- formation gives power to decision makers.

For the last twenty years, different kinds of information systems are developed for different purposes, depending on the need of the business. Transaction Process Systems (TPS) function in operational level to process large amount of data for routine business transactions of the organization, Office Automation Systems (OAS) support data workers and Knowledge Work Systems (KWS) support professional workers. Higher-level systems include Management Information Systems (MIS) and Decision Support Systems (DSS). Expert System (ES) applies the expertise of decision makers to solve specific, unstructured problems. At the strategic level of management, there is Executive Support Systems (ESS). Group Decision Support Systems (GDSS) and the more generally described Computer Supported Collaborative Work (CSCW) systems aid group level decision making of a semi structured or unstructured decision.

Figure 1: Components of Decision-Making

DECISION MAKING DEFINITION:-The thoughtprocessof selecting alogicalchoicefrom the availableoptions. When trying to make a gooddecision, apersonmustweightthe positives and negatives of each option, and consider all the alternatives. Foreffectivedecision making, a person must beabletoforecastthe outcome of each option as well, and based on all theseitems, determine which option is the best for that particular situation.Small business owners and managers make decisions on a daily basis, addressing everything from day-to-day operational issues to long-range strategic planning. The decision-making process of a manager can be broken down into six distinct steps. Although each step can be examined at length, managers often run through all of the steps quickly when making decisions. Understanding the process of managerial decision-making can improve your decision-making effectiveness.

Identify Problems-The first step in the process is to recognize that there is a decision to be made. Decisions are not made arbitrarily; they result from an attempt to address a specific problem, need or opportunity.A supervisor in a retail shop may realize that he has too many employees on the floor compared with the day's current sales volume, for example, requiring him to make a decision to keep costs under control.

Seek Information-Managers seek out a range of information to clarify their options once they have identified an issue that requires a decision. Managers may seek to determine potential causes of a problem, the people and processes involved in the issue and any constraints placed on the decision-making process.

Brainstorm SolutionsHaving a more complete understanding of the issue at hand, managers move on to make a list of potential solutions. This step can involve anything from a few seconds of thought to a few months or more of formal collaborative planning, depending on the nature of the decision.

Choose an AlternativeManagers weigh the pros and cons of each potential solution, seek additional information if needed and select the option they feel has the best chance of success at the least cost. Consider seeking outside advice if you have gone through all the previous steps on your own; asking for a second opinion can provide a new perspective on the problem and your potential solutions.

Implement the PlanThere is no time to second guess yourself when you put your decision into action. Once you have committed to putting a specific solution in place, get all of your employees on board and put the decision into action with conviction. That is not to say that a managerial decision cannot change after it has been enacted; savvy managers put monitoring systems in place to evaluate the outcomes of their decisions.

Evaluate OutcomesEven the most experienced business owners can learn from their mistakes. Always monitor the results of strategic decisions you make as a small business owner; be ready to adapt your plan as necessary, or to switch to another potential solution if your chosen solution does not work out the way you expected.

CONCEPT OF DECISION-MAKING Decision-making is a cognitive process that results in the selection of a course of action among several alternative scenarios. Decision-making is a daily activity for any human being. There is no exception about that. When it comes to business organizations, decision-making is a habit and a process as well. Effective and successful decisions result in profits, while unsuccessful ones cause losses. Therefore, corporate decision-making is the most critical process in any organization. In a decision-making process, we choose one course of action from a few possible alternatives. In the process of decision-making, we may use many tools, techniques, and perceptions. In addition, we may make our own private decisions or may prefer a collective decision. Usually, decision-making is hard. Majority of corporate decisions involve some level of dissatisfaction or conflict with another party.

DECISION-MAKING PROCESSFollowing are the important steps of the decision-making process. Each step may be supported by different tools and techniques.

Figure 2: Flowchart depicting Decision-Making processStep 1: Identification of the Purpose of the DecisionIn this step, the problem is thoroughly analysed. There are a couple of 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?Step 2: Information GatheringA problem of an organization will have many stakeholders. In addition, there can be dozens of factors involved and affected by the problem.In the process of solving the problem, you will have to gather as much as information related to the factors and stakeholders involved in the problem. For the process of information gathering, tools such as 'Check Sheets' can be effectively used.Step 3: Principles for Judging the AlternativesIn this step, the baseline criteria for judging the alternatives should be set up. When it comes to defining the criteria, organizational goals as well as the corporate culture should be taken into consideration.As an example, profit is one of the main concerns in every decision making process. Companies usually do not make decisions that reduce profits, unless it is an exceptional case. Likewise, baseline principles should be identified related to the problem in hand.Step 4: Brainstorm and Analyse the ChoicesFor this step, brainstorming to list down all the ideas is the best option. Before the idea generation step, it is vital to understand the causes of the problem and prioritization of causes.For this, you can make use of Cause-and-Effect diagrams and Pareto Chart tool. Cause-and-Effect diagram helps you to identify all possible causes of the problem and Pareto chart helps you to prioritize and identify the causes with the highest effect. Then, you can move on generating all possible solutions (alternatives) for the problem in hand.Step 5: Evaluation of AlternativesUse your judgment principles and decision-making criteria to evaluate each alternative. In this step, experience and effectiveness of the judgment principles come into play. You need to compare each alternative for their positives and negatives.Step 6: Select the Best AlternativeOnce you go through from Step 1 to Step 5, this step is easy. In addition, the selection of the best alternative is an informed decision since you have already followed a methodology to derive and select the best alternative.Step 7: Execute the decision:Convert your decision into a plan or a sequence of activities. Execute your plan by yourself or with the help of subordinates.Step 8: Evaluate the Results:Evaluate the outcome of your decision. See whether there is anything you should learn and then correct in future decision making. This is one of the best practices that will improve your decision-making skills. IMPORTANCE OF DECISION MAKING:-1.Implementation of managerial function: Without decision making different managerial function such as planning, organizing, directing, controlling, staffing cant be conducted. In other words, when an employee does, s/he does the work through decision making function. Therefore, we can say that decision is important element to implement the managerial function.2.Pervasiveness of decision making:the decision is made in all managerial activities and in all functions of the organization. It must be taken by all staff. Without decision making any kinds of function is not possible. So it is pervasive.3.Evaluation of managerial performance:Decisions can evaluate managerial performance. When decision is correct it is understood that the manager is qualified, able and efficient. When the decision is wrong, it is understood that the manager is disqualified. So decision making evaluate the managerial performance.4.Helpful in planning and policies:Any policy or plan is established through decision making. Without decision making, no plans and policies are performed. In the process of making plans, appropriate decisions must be made from so many alternatives. Therefore decision making is an important process which is helpful in planning.5.Selecting the best alternatives:Decision making is the process of selecting the best alternatives. It is necessary in every organization because there are many alternatives. So decision makers evaluate various advantages and disadvantages of every alternative and select the best alternative.6.Successful; operation of business:Every individual, departments and organization make the decisions. In this competitive world; organization can exist when the correct and appropriate decisions are made. Therefore correct decisions help in successful operation of business.MANAGEMENT INFORMATION SYSTEM & DECISION MAKING:-Thetype of informationrequired by decision makers in a company is directly related to: the level of management decision making the amount of structure in the decision situations managers faceThelevels of management decision makingthat must be supported by information technology in a successful organization (independently of its size, shape, and participants), are often shown as a managerial pyramidStrategic management: As part of a strategic planning process top executivesi. develop overall organizational goals, strategies, policies, andii. monitor the strategic performance of the organization and its overall direction in the political, economic, and competitive business environmentTactical management: Business unit managers and business professionals in self-directed teamsi. Develop short- and medium-range plans, schedules, budgets and specify policies, procedures, and business objectives for their sub-units of the company, andii. Allocate resources and monitor the performance of their organizational sub-units, including departments, divisions, process teams, project teams, and other workgroups.Operational management: Operating managers and members of self-directed teamsi. Develop short-range plans (e.g. weekly production schedules), andii. Direct the use of resources and the performance of tasks according to procedures and within budgets and schedules they establish for the teams and other workgroups of the organization.

Figure 3: Levels of Decision MakingDecision maker at different levels of the organization are making more or less structured decisions. Typically there are three types ofdecision structure:

Unstructured decisions(usually related to the long-term strategy of the organization);Semi-structured decisions(some decision procedures can be pre-specified but not enough to lead to a definite recommended decision);Structured decisions(the procedure to follow, when a decision is needed, can be specified in advance).With respect to theinformation system, it can be any organized combination of people, hardware, software, communication networks, data resources, and policies and procedures that stores, retrieves, transforms, and disseminates information in an organization.There arethree vital rolesthat information systems can perform for a business enterprise: support of business processes and operations, support of decision making by employees and managers, and support of strategies for competitive advantage see the figure below

Figure 4: Role of Information Systems

Theapplications of information systemsthat are implemented in today's business world can be classified as either operations or management information systems see the figure, below

Figure 5: Classification of applications of information systems

Operations Support Systems(OSS) produce a variety of information products for internal and external use, such as processing business transactions, controlling industrial processes, supporting enterprise communications and collaborations, and updating corporate databases effectively. They do not emphasize the specific information products that can best be used by managers. Further processing by management information systems is usually required.The management classifications of information systems can be structured in four main groups of systems Management Information Systems(MIS): provide information in the form of reports and displays to managers and many business professionals that support their day-to-day decision-making needs. Usually the information has been specified in advance to adequately meet the expectations on operational and tactical levels of the organization, where the decision making situations are more structured and better defined. Decision Support Systems(DSS) are computer-based information systems that provide interactive information support to managers and business professionals during the decision-making process. DSS use analytical models, specialized databases, a decision maker's own insights and judgments, and an interactive, computer-based modelling process to support semi-structured business decisions. Executive Information Systems(EIS) orExecutive Support Systems(ESS) are information systems that combine many of the futures of MIS and DSS. Here the information is presented in forms tailored to the preferences of the executives using the system, such as graphical user interface, customized to the executives graphics displays, exception reporting, trend analysis, and abilities to 'drill-down' and retrieve displays of related information quickly at lower levels of detail. Specialized Processing Systems(PS) are information systems characterized as functional business systems, strategic information systems, knowledge management systems, and expert systems.

TPSOASMISKWSDSSESSORGANIZATIONAL LEVELTYPE OFDECISIONOPERATIONALKNOWLEDGEMANAGEMENTSTRATEGICSTRUCTUREDACCOUNTS RECEIVABLEELECTRONIC PRODUCTIONSCHEDULINGCOST OVERRUNSSEMI-BUDGETSTRUCTUREDPREPARATION PROJECTSCHEDULINGFACILITYLOCATIONUNSTRUCTUREDPRODUCT DESIGNNEW PRODUCTSNEW MARKETS

Figure 6: Organisational usage of Information SystemIt is important to realize that business applications of information systems in the real world are typically integrated combinations of all these types of information systems. In practice, all these different types and roles of information systems are combined into integrated orCross-Functional Business Information Systemsthat provide a variety of functions. Thus, most information systems are designed to produce information and support decision making for various levels of management and business functions, as well as perform record-keeping and transaction-processing chores. Whenever you analyse or work with an information system, you probably see that it provides information for a variety of managerial levels and business functions. ROLE OF MIS IN DECISION MAKING:-Management information systems can help you make valid decisions by providing accurate and up-to-date information and performing analytic functions. You have to make sure the management information system you choose can work with the information formats available in your company and has the features you need. Suitable management information systems can structure the basic data available from your company operations and records into reports to present you with guidance for your decisions. Management information systems combine hardware, software and network products in an integrated solution that provides managers with data in a format suitable for analysis, monitoring, decision-making and reporting. The system collects data, stores it in a database and makes it available to users over a secure network. Information-When you base your decisions on data available from management information systems, they reflect information that comes from the operations of your company. Management information systems take data generated by the working level and organize it into useful formats. Management information systems typically contain sales figures, expenses, investments and workforce data. If you need to know how much profit your company has made each year for the past five years to make a decision, management information systems can provide accurate reports giving you that information. Managers need rapid access to information to make decisions about strategic, financial, marketing and operational issues. Companies collect vast amounts of information, including customer records, sales data, market research, financial records, manufacturing and inventory data, and human resource records. However, much of that information is held in separate departmental databases, making it difficult for decision makers to access data quickly. A management information system simplifies and speeds up information retrieval by storing data in a central location that is accessible via a network. The result is decisions that are quicker and more accurate.Data Collection-Management information systems bring together data from inside and outside the organization. By setting up a network that links a central database to retail outlets, distributors and members of a supply chain, companies can collect sales and production data daily, or more frequently, and make decisions based on the latest information.Scenarios-The capability to run scenarios is a key decision-making tool. Some management information systems have this feature built in, while others can provide the information required for running scenarios on other applications, such as spreadsheets. Your decision is influenced by what happens if you decide a certain way. What-if scenarios show you how different variables change when you make a decision. You can enter reduced staff levels or increased promotion budgets and see what happens to revenue, expenses and profit for different levels of cuts or increases. Management information systems play a critical role in making realistic scenarios possible.Collaboration-In situations where decision-making involves groups, as well as individuals, management information systems make it easy for teams to make collaborative decisions. In a project team, for example, management information systems enable all members to access the same essential data, even if they are working in different locations.Projections-Any decisions you make result in changes in the projected company results and may require modifications to your business strategy and overall goals. Management information systems either have trend analysis built in or can provide information that lets you carry out such an analysis. Typical business strategies include projections for all fundamental operating results. A trend analysis allows you to show what these results would be in the current situation and how they will change once you have implemented the decisions you have taken. The new values form the basis of your strategic approach going forward.Management information systems help decision-makers understand the implications of their decisions. The systems collate raw data into reports in a format that enables decision-makers to quickly identify patterns and trends that would not have been obvious in the raw data. Decision-makers can also use management information systems to understand the potential effect of change. A sales manager, for example, can make predictions about the effect of a price change on sales by running simulations within the system and asking a number of what if the price was questions.Implementation-While you make your decisions with specific goals in mind and have the documentation from management information systems and trend analysis to support your expectations, you have to track company results to make sure they develop as planned. Management information systems give you the data you need to determine whether your decisions have had the desired effect, or whether you have to take corrective action to reach your goals. If specific results are not on track, you can use management information systems to evaluate the situation and decide to take additional measures if necessary.Presentation-The reporting tools within management information systems enable decision-makers to tailor reports to the information needs of other parties. If a decision requires approval by a senior executive, the decision-maker can create a brief executive summary for review. If managers want to share the detailed findings of a report with colleagues, they can create full reports and provide different levels of supplementary data.

CHAPTER 2LITERATURE REVIEW

LITERATURE REVIEW:-Classical theories of choice in organisations emphasise decision making as the making of rational choices on the basis of expectations about the consequences of action for prior objectives, and organisational forms as instruments for making those choices . It is likely that most organisations would like to think they and their employees follow such rational processes; in practice it is unlikely to be frequently achieved. The gap between descriptive (what we are observed to do) and normative (what we should do) decision making is extensive and in fact has widened over recent years. There are potentially two paths by which the gap may be narrowed. Firstly, and the view Payne et al. appear to take, is to attempt to persuade decision makers to adopt more normative techniques. Although this could certainly improve decision making, convincing decision makers to do so is likely to be a significant hurdle. Conversely, normative theories may be humanised by incorporating aspects of human limitations and behaviour. Managers face decisions every day involving uncertainty. If a company is considering expanding a facility, there is uncertainty about whether future demand will be high enough to make the expansion financially attractive. The decision to expand, however, must be made before it is known what future demand will be. The uncertainty in demand implies the risk that the company will be hurt financially. Quantitative models can provide tremendous insight and assistance in decision making. Unfortunately, many quantitative models ignore the uncertainty present in the real situations. Sometimes this uncertainty is taken into account after a model is built when some different what-if scenarios are considered, or a sensitivity analysis is conducted, using methods similar to those discussed in Supplement A. However, oftentimes there is uncertainty about a number of factors, and it is difficult to do a complete scenario analysis. Computer simulation is a methodology that allows one to model the uncertainty directly and obtain a clear picture of the effect of that uncertainty on the output quantities of a model. That is, simulation allows a decision maker to accurately determine the effects of the uncertainty present in a situation.

There are almost limitless ways the power of simulation can be used to bring insight into managerial decision making, both in traditional OM areas and throughout the organization. A few decision situations in which simulation can and has been used effectively are listed here. Financial analysis for new products, expansions, or any effort involving the expenditure of funds now in the hope of future (but uncertain) payoffs. Project planning and scheduling. Activity times are almost always uncertain, which causes the actual completion time of the project to be uncertain. Determining the customer service impacts of adding or removing serving capacity. For example, what will be the effect of adding a second drive-through window at a banking facility? Evaluating different inventory policies while taking into account demand uncertainty. Evaluating different plant and supply chain policies, such as scheduling and route assignments.What are Decision Support Systems (DSS)?Decision support consultants are employed or decision support systems (DSS) are implemented in order to support decision-making in an organisation. This assumes that the way in which decision-making actually takes place in the organisation is understood. There are many models of decision-making. People with a background in quantitative analysis would typically have been exposed to rational decision-making methods, such as Simon (1977) four-step decision model that incorporates intelligence, decision, choice, review. This process is often accompanied by the calculation of the subjective expected utility (SEU) or another way of ranking alternatives to facilitate choosing the best option. It has been observed that the outputs of decision support projects, often packaged as decision support systems, are not used to support decision-making in the way that was intended. This could imply some discrepancy between the decision-making process that is being assumed or modelled and the way decision-making occurs in practise. In order to test assumptions about decision-making & the use of decision support technology, the literature on decision-making was studied and compared to the way that a number of mangers make decisions in practise.

What is Monte-Carlo Simulation?Risk analysis is a part of every decision we make. We are constantly faced with uncertainty, ambiguity & variability. And even though we have unprecedented access to information, we cant accurately predict the future. Monte Carlo Simulation lets you see the all possible outcomes of your decision & assess the impact of risk, allowing for better decision making under uncertainty.Monte Carlo simulation is a way to represent and analyse risk and uncertainty.It was called "Monte Carlo" after thefamous casino in thePrincipality of Monaco on the French Riviera established in 1856. Insteadof aroulette wheel or cards,Monte Carlosimulation generates random numbers using a (pseudo)random number algorithm. In Monte Carlosimulation, the uncertainty in key input quantities is represented as a probability distribution.In standard Monte Carlo simulation, software samples a random value from each input distribution and runs the model using those values. After repeating the process a number of times (typically 100 to 10,000), it estimates probability distributions for the uncertain outputs of the model from the random sample of output values. The larger the sample size, the more accurate the estimation of the output distributions.There are variants of Monte Carlo simulation that can be more efficient than simple random sampling -- they converge faster reaching higher accuracy with a smaller sample size. Latin hypercube sampling (LHS)divides up each uncertain input intonequiprobable intervals. When generating its nruns, it samples exactly once from each interval. In so doing, it achieves a more uniform sampling over each input distribution than standard Monte Carlo, where the natural randomness usually results in more clumped sampling.Forrandom LHS, it samples at random from each interval, using the underlying distribution, and results are guaranteed to be unbiased. ForMedian LHS, it uses the median of each of thenintervals. Median LHS is notguaranteedto be unbiased, but in the vast majority of real applications it is unbiased and it usually converges faster than simple Monte Carlo or random LHS.

CHAPTER 3RESEARCH METHODOLOGY

RESEARCH METHODOLOGY:-a) TOPIC OF RESEARCH: - Study of Simulation modelling for decision makingb) OBJECTIVES:- To study different types of decision making structures used in organization To study the role of Information Systems in decision making To evaluate various Decision Support System (DSS) models & tools required for rational decision making. To gain more insight to the use of Simulation Modelling to a greater extent by the organizations. c) SIGNIFICANCE OF STUDY:-Decision making is an integral part of the functioning of any organization. To facilitate decision making in this ever-competitive world it is imperative that managers have the right information at the right time to bridge the gap between need and expectation. To facilitate better flow of information adequate Management Information Systems (MIS) is the need of the hour. Thus it is important to have an understanding of the MIS followed in an organization by all levels of management in order to take decisions. A management information system collects and processes data (information) and provides it to managers at all levels who use it for decision making, planning, program implementation, and control. The MIS has many roles to perform like the decision support role, the performance monitoring role and the functional support role. Thus also the gaining importance of simulation modelling in DSS should be imperatively studied so as to get a better understanding of the changing technical scenario in order to compete with the Techno-global world.

d) SCOPE OF RESEARCH:-It is a desk research which explores the possibility of determining the effectiveness of Simulation Modelling in particular and also it is purely based on Secondary Data collection so it is subject to the data already published in various research papers & websites so forth. The reliability of data is rather perplexing and is constrained strictly on its availability. Thus this research is purely study based and the conclusions are general views about the study.e) DATA COLLECTION:- Data is facts and statistics collected together for reference or analysis.i. Types of Data:There are two different types of data that we use when we are carrying our research projects. These two different types of data are called Primary and Secondary data collection.a. Primary Data:-Primary data is data that we collect ourselves during the period of our research e.g. Questionnaires, Observations, Interviews and so on. We then use the data we have collected and noted down to begin the next stage of our research which is the theory making and the understanding of what we are researching.Primary data is best used for ever evolving research because different factors play roles in things we research and can lead to varying results depending on the factor and how much of a role it plays on the research.b. Secondary Data:-Secondary data is data that has already been collect and we use for reference or to gain knowledge from other peoples experiences e.g. published books, Government publications, Journals and the internet. We then use this data to add to the Primary data that we have collected and use it to combine different peoples opinions and base a theory with evidence to back this point up.Secondary data is best used to add other existing evidence and proof to the Primary data that we have collected, we are better using Secondary data as reference and to gain the knowledge that we need to begin our own research processes. Sources of Data:-Primary Data Sources: SurveySurvey is most commonly used method in social sciences, management, marketing and psychology to some extent. Surveys can be conducted in different methods. Questionnaire Questionnaire is the most commonly used method in survey. Questionnaires are a list of questions either an open-ended or close -ended for which the respondent give answers. Questionnaire can be conducted via telephone, mail, live in a public area, or in an institute, through electronic mail or through fax and other methods. InterviewInterview is a face-to-face conversation with the respondent. It is slow, expensive, and they take people away from their regular jobs, but they allow in-depth questioning and follow-up questions. ObservationsObservations can be done while letting the observing person know that he is being observed or without letting him know. Observations can also be made in natural settings as well as in artificially created environment. Secondary Data Sources: Published Printed Sources There are varieties of published printed sources. Their credibility depends on many factors. For example, on the writer, publishing company and time and date when published. New sources are preferred and old sources should be avoided as new technology and researches bring new facts into light.

BooksBooks are available today on any topic that you want to research. The uses of books start before even you have selected the topic. After selection of topics books provide insight on how much work has already been done on the same topic and you can prepare your literature review. Books are secondary source but most authentic one in secondary sources. Journals/periodicalsJournals and periodicals are becoming more important as far as data collection is concerned. The reason is that journals provide up-to-date information which at times books cannot and secondly, journals can give information on the very specific topic on which you are researching rather talking about more general topics. Magazines/NewspapersMagazines are also effective but not very reliable. Newspaper on the other hand is more reliable and in some cases the information can only be obtained fromnewspapersas in the case of some political studies. Published Electronic Sources As internet is becoming more advance, fast and reachable to the masses; it has been seen that much information that is not available in printed form is available on internet. In the past the credibility of internet was questionable but today it is not. The reason is that in the past journals and books were seldom published on internet but today almost everyjournaland book is available online. Some are free and for others you have to pay the price. E-journals: e-journals are more commonly available than printed journals. Latestjournalsare difficult to retrieve without subscription but if your university has an e-library you can view any journal, print it and those that are not available you can make an order for them. General Websites; Generally websites do not contain very reliable information so their content should be checked for thereliabilitybefore quoting from them. Weblogs: Weblogs are also becoming common. They are actually diaries written by different people. These diaries are as reliable to use as personal written diaries.ii. Data Collection Methods:-This research being purely desk research uses Secondary Data Collection method solely.Sources: Books Published electronic sources E-journals General Websites Weblogs E-Research documents

CHAPTER 4DATA ANALYSIS

DATA ANALYSIS:-PROCESS AND MODELING IN DECISION-MAKING:There are two basic models in decision-making: Rational models Normative modelThe 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.A 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.The normative model of decision-making considers constraints that may arise in making decisions, such as time, complexity, uncertainty, and inadequacy of resources. According to this model, 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. Satisficing - A person may choose a solution that is just "good enough".

Dynamic Decision-Making:Dynamic decision-making (DDM) 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.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. If a particular quantity has no bearing on a decision or prediction, then the conditions relating to quantity could be eliminated, thus simplifying the decision making process.Sensitivity analysis also helps in some other situations, like: Resource optimization Future data collections Identifying critical assumptions To optimize the tolerance of manufactured partsStatic and Dynamic ModelsStatic models: Show the value of various attributes in a balanced system. Work best in static systems. Do not take into consideration the time-based variances. Do not work well in real-time systems however, it may work in a dynamic system being in equilibrium Involve less data. Are easy to analyse. Produce faster results.Dynamic models: Consider the change in data values over time. Consider effect of system behaviour over time. Re-calculate equations as time changes. Can be applied only in dynamic systems.Simulation TechniquesSimulation 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 TechniquesOperational 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.It encompasses techniques such as simulation, mathematical optimization, queuing theory, stochastic-process models, econometric methods, data envelopment analysis, neural networks, expert systems, decision analysis, and the analytic hierarchy process.OR techniques describe a system by constructing its mathematical models.

Heuristic ProgrammingHeuristic 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.Heuristics take a 'guess' approach to problem solving, yielding a 'good enough' answer, rather than finding a 'best possible' solution.Group Decision-MakingIn group decision-making, various individuals in a group take part in collaborative decision-making.Group Decision Support System (GDSS) is a decision support system that provides support in decision making by a group of people. It facilitates the free flow and exchange of ideas and information among the group members. Decisions are made with a higher degree of consensus and agreement resulting in a dramatically higher likelihood of implementation.Following are the available types of computer based GDSSs: Decision Network:This type helps the participants to communicate with each other through a network or through a central database. Application software may use commonly shared models to provide support. Decision Room:Participants are located at one place, i.e. the decision room. The purpose of this is to enhance participant's interactions and decision-making within a fixed period of time using a facilitator. Teleconferencing:Groups are composed of members or sub groups that are geographically dispersed; teleconferencing provides interactive connection between two or more decision rooms. This interaction will involve transmission of computerized and audio visual information.

TYPES OF DECISION SUPPORT SYSTEM (DSS):-Decision Support Systems (DSS) are a class of computerized information system that support decision-making activities. DSS are interactive computer-based systems & subsystems intended to help decision makers use communications technologies, data, documents, knowledge and/or models to complete decision process tasks.A decision support system may present information graphically and may include an expert system or artificial intelligence (AI). It may be aimed at business executives or some other group of knowledge workers.Typical information that a decision support application might gather and present would be, (a) Accessing all information assets, including legacy and relational data sources; (b) Comparative data figures; (c) Projected figures based on new data or assumptions; (d) Consequences of different decision alternatives, given past experience in a specific context.There are a number of Decision Support Systems. These can be categorized into five types: Communication-driven DSS:Most communications-driven DSSs are targeted at internal teams, including partners. Its purpose are to help conduct a meeting, or for users to collaborate. The most common technology used to deploy the DSS is a web or client server. Examples: chats and instant messaging softwares, online collaboration and net-meeting systems. Data-driven DSS: Most data-driven DSSs are targeted at managers, staff and also product/service suppliers. It is used to query a database or data warehouse to seek specific answers for specific purposes. It is deployed via a main frame system, client/server link, or via the web. Examples: computer-based databases that have a query system to check (including the incorporation of data to add value to existing databases. Document-driven DSS: Document-driven DSSs are more common, targeted at a broad base of user groups. The purpose of such a DSS is to search web pages and find documents on a specific set of keywords or search terms. The usual technology used to set up such DSSs are via the web or a client/server system. Knowledge-driven DSS:Knowledge-driven DSSs or 'knowledgebase' are they are known, are a catch-all category covering a broad range of systems covering users within the organization setting it up, but may also include others interacting with the organization - for example, consumers of a business. It is essentially used to provide management advice or to choose products/services. The typical deployment technology used to set up such systems could be client/server systems, the web, or software running on stand-alone PCs. Model-driven DSS: Model-driven DSSs are complex systems that help analyse decisions or choose between different options. These are used by managers and staff members of a business, or people who interact with the organization, for a number of purposes depending on how the model is set up - scheduling, decision analyses etc. These DSSs can be deployed via software/hardware in stand-alone PCs, client/server systems, or the web.SIMULATION MODEL BASED DECISION MAKING:-Simulation is a broad term that refers to an approach for imitating the behaviour of an actual or anticipated human or physical system. The terms simulation and model, especially quantitative and behavioural models, are closely linked. From my perspective, a model shows the relationships and attributes of interest in the system under study. A quantitative or behavioural model is by design a simplified view of some of the objects in a system. A model used in a simulation can capture much detail about a specific system, but how complex the model is or should be depends upon the purpose of the simulation that will be "run" using the model. With a simulation study and when simulation provides the functionality for a DSS, multiple tests, experiments or "runs" of the simulation are conducted, the results of each test are recorded and then the aggregate results of the tests are analysed to try to answer specific questions. In a simulation, the decision variables in the model are the inputs that are manipulated in the tests. Simulation-based DSS refers to a category of DSS in which simulation is used as one of the (main) components of the system. The decision problem consists of a set of available decision alternatives and an objective function representing the preferences of the decision maker. The alternatives are compared using an appropriate simulation model, which may incorporate uncertain data. The decision alternatives constitute the input parameters of the simulation model. Then, based on the simulation output, the optimal decision is distinguished or alternatives are ranked. Advantages associated with the use of simulation in DSS and optimization problems are widely acknowledged in the literature. Simulation models can be used for extremely complex problems, where analytical approaches are not available. They explicitly account for physical processes and give a more complete description of reality. They permit to incorporate various interactions and correlations and capture more of the real world complexities. Simulation models have the ability to incorporate random events and imperfect information. They are more general than analytical models for uncertainty and usually have fewer assumptions and impose no restrictions on the probability distributions involved. Simulation models can conveniently be combined with other analytical, or numerical methods and provide a single integrated model. Simulation approaches can readily be combined with different optimization methods and techniques.Simulationis used to model efficiently a wide variety of systems that are important to managers. A simulation is basically an imitation, a model that imitates a real-world process or system. In business and management, decision makers are often concerned with the operating characteristics of a system. One way to measure or assess the operating characteristics of a system is to observe that system in actual operation. However, in many types of situations the cost of direct observation can be very high. Furthermore, changing some of the relationships or parameters within a system on an experimental basis may mean waiting a considerable amount of time to collect results on all the combinations that are of concern to the decision maker.In business and management, a simulation is amathematicalimitation of a real-world system. The use of computers to conduct simulations is not essential from a theoretical standpoint. However, most simulations are sufficiently complex from a practical standpoint to require the use of computers in running them. A simulation can also be considered to be an experimental process. In a set of experimental runs, the decision maker actively varies some of the parameters or relationships in the system. If the mathematical model behind the simulation is valid, the results of the simulation runs willimitatethe results of the real system if it were to operate over some period of time.In order to better understand the fundamental issues of simulation, an example is useful. Suppose a regional medical centre seeks to provide air ambulance service to trauma and burn victims over a wide geographic area. Issues such as how many helicopters would be best and where to place them would be in question. Other issues such as scheduling of flight crews and the speed and payload of various types of helicopters could also be important. These represent decision variables that are to a large degree under the control of the medical centre. There are uncontrollable variables in this situation as well. Examples are the weather and the prevailing accident and injury rates throughout the medical centres service region.Given the random effects of accident frequencies and locations, the analysts for the medical centre would want to decide how many helicopters to acquire and where to place them. Adding helicopters and flight crews until the budget is spent is not necessarily the best course of action. Perhaps two strategically placed helicopters would serve the region as efficiently as four helicopters of some other type scattered haphazardly about. Analysts would be interested in such things as operating costs, response times, and expected numbers of patients who would be served. All of these operating characteristics would be impacted by injury rates, weather, and any other uncontrollable factors as well as by the variables they are able to control.The medical centre could run their air ambulance system on a trial-and-error basis for many years before they had any reasonable idea what combinations of resources would work well. Not only might they fail to find the best or near-best combination of controllable variables, but also they might very possibly incur an excessive loss of life as a result of poor resource allocation. For these reasons, this decision-making situation would be an excellent candidate for a simulation approach. Analysts could simulate having any number of helicopters available. To the extent that their model is valid, they could identify the optimal number to have to maximize service, and where they could best be stationed in order to serve the population of seriously injured people who would be distributed about the service region. The fact that accidents can be predicted only statistically means that there would be a strong random component to the service system and that simulation would therefore be an attractive analytical tool in measuring the system's operating characteristics.MONTE CARLO SIMULATION: There are several different strategies for developing a working simulation, but two are probably most common. The first is the Monte Carlo simulation approach. The second is the event-scheduling approach. Monte Carlo simulation is applied where the passage of time is not incorporated into the simulation model. Consider again the air ambulance example. If the simulation is set up to imitate an entire month's worth of operations all at once, it would be considered aMonte Carlo simulation. A random number of accidents and injuries would generate a random number of flights with some sort of average distance incorporated into the model. Operating costs and possibly other operating values sought by the analysts would be computed. The advantage of Monte Carlo simulation is that it can be done very quickly and simply. Thus, many months of operations could be simulated in the ambulance example. From the many months of operational figures, averages and distributions of costs could readily be acquired. Unfortunately, there is also a potentially serious disadvantage to the Monte Carlo simulation approach. If analysts ignore the passage of time in designing the simulation, the system itself may be oversimplified. In the air ambulance example, it is possible to have a second call come in while a flight is in progress which could force a victim to wait for a flight if no other helicopter is available. A Monte Carlo simulation would not account for this possibility and hence could contribute to inaccurate results. This is not to say that Monte Carlo simulations are generally flawed. Rather, in situations where the passage of time is not a critical part of the system being modelled, this approach can Problem Solving and Decision Making with Simulation Software

SIMULATION MODEL SCHEMATIC:-Fixed (Known)Inputs

Outputs/Performance MeasuresRandom (Uncertain) InputsSimulation model

Decision Variables

Figure 7: Simulation modelling processSimulation is a decision analysis and support tool.Simulation softwareallows you to evaluate, compare and optimize alternative designs, plans and policies. As such, it provides a tool for explaining and defending decisions to various stakeholders.Simulationshould be used when the consequences of a proposed action, plan or design cannot bedirectly and immediately observed(i.e., the consequences are delayed in time and/or dispersed in space) and/or it is simply impractical or prohibitively expensive to test the alternatives directly. For example, when implementing astrategic plan for a company, the impacts are likely to take months (or years) to materialize.Simulation is particularly valuable when there is significant uncertainty regarding the outcome or consequences of a particular alternative under consideration.Probabilistic simulationallows you deal with this uncertainty in a quantifiable way.Perhaps most importantly, simulation should be used when the system under consideration has complex interactions and requires the input from multiple disciplines. In this case, it is difficult for any one person to easily understand the system.A simulation model can act as the framework to integrate the various components in order to better understand their interactions. As such, it becomes a management tool that keeps you focused on the "big picture" without getting lost in unimportant details. Because simulation is such a powerful tool to assist in understanding complex systems and to support decision-making, a wide variety of approaches and tools exist.Manyspecial purpose simulatorsexist to simulate very specific types of systems. For example, tools exist for simulating the movement of water (and contaminants) in an estuary, the evolution of a galaxy, or the exchange rates for a set of currencies. The key attribute of these tools is that they are highly specialized to solve a particular type of problem. In many cases, these tools require great subject-matter expertise to use.In other cases, however, the system being simulated may be so highly specified that using the tools is quite simple (i.e., the user is presented with a very limited number of options).Other tools are not specialized to a particular type of problem. Rather, they are "tool kits" orgeneral purpose frameworksfor simulating a wide variety of systems.There are a variety of such tools, each tailored for a specific type of problem. What they all have in common, however, is that they allow the user to model how a system might evolve or change over time. Such frameworks can be thought of as high-level programming languages that allow the user to simulate many different kinds of systems in a flexible way.Perhaps the simplest and most broadly used general purpose simulator is thespreadsheet. Although spreadsheets are inherently limited by their structure in many ways (e.g., representing complex dynamic processes is difficult, they cannot display the model structure graphically, and they require special add-ins to represent uncertainty), because of their ubiquity, they are very widely used for simple simulation projects (particularly in the business world).Other general purpose tools exist that are better able to represent complex dynamics, as well as provide a graphical mechanism for viewing the model structure (e.g., an influence diagram or flow chart of some type). Although these tools are generally harder to learn to use than spreadsheets (and are typically more expensive), these advantages allow them to realistically simulate larger and more complex systems than can be done in a spreadsheet.The general purpose tools can be broadly categorized as follows:Discrete Event SimulatorsThese toolsrely on a transaction-flow approach to modelling systems. Models consist of entities (units of traffic), resources (elements that service entities), and control elements (elements that determine the states of the entities and resources). Discrete simulators are generally designed for simulating processes such as call centres, factory operations, and shipping facilities in which the material or information that is being simulated can be described as moving in discrete steps or packets. They are not meant to model the movement of continuous material (e.g., water) or represent continuous systems that are represented by differential equations.Agent-Based SimulatorsThis is a special class of discrete event simulator in which the mobile entities are known as agents. Whereas in a traditional discrete event model the entities only have attributes (properties that may control how they interact with various resources or control elements), agents have both attributes and methods (e.g., rules for interacting with other agents). An agent-based model could, for example, simulate the behaviour of a population of animals that are interacting with each other.Continuous SimulatorsThis class of tools solves differential equations that describe the evolution of a system using continuous equations. These type of simulators are most appropriate if the material or information that is being simulated can be described as evolving or moving smoothly and continuously, rather than in infrequent discrete steps or packets. For example, simulation of the movement of water through a series of reservoirs and pipes can most appropriately be represented using a continuous simulator. Continuous simulators can also be used to simulate systems consisting of discrete entities if the number of entities is large so that the movement can be treated as a flow. Hybrid SimulatorsThese tools combine the features of continuous simulators and discrete simulators. That is, they solve differential equations, but can superimpose discrete events on the continuously varying system.GoldSim is a hybrid simulator.

Who uses Monte Carlo simulation?Many companies use Monte Carlo simulation as an important part of their decision-making process. Here are some examples. General Motors, Proctor and Gamble, Pfizer, Bristol-Myers Squibb, and Eli Lilly use simulation to estimate both the average return and the risk factor of new products. At GM, this information is used by the CEO to determine which products come to market. GM uses simulation for activities such as forecasting net income for the corporation, predicting structural and purchasing costs, and determining its susceptibility to different kinds of risk (such as interest rate changes and exchange rate fluctuations). Lilly uses simulation to determine the optimal plant capacity for each drug. Proctor and Gamble uses simulation to model and optimally hedge foreign exchange risk. Sears uses simulation to determine how many units of each product line should be ordered from suppliersfor example, the number of pairs of Dockers trousers that should be ordered this year. Oil and drug companies use simulation to value "real options," such as the value of an option to expand, contract, or postpone a project. Financial planners use Monte Carlo simulation to determine optimal investment strategies for their clients retirement.

EXAMPLE OF MONTE-CARLO SIMULATION:-

CHAPTER 5RESEARCH FINDINGS, RECOMMENDATIONS & CONCLUSION

RESEARCH FINDINGS:-Why Monte Carlo SimulationThe most importantadvantagesof Monte Carlo include: The probability distributions within the model can be easily and flexibly used, without the need to approximate them; Correlations and other relations and dependencies (such as if statements) can be modelled without difficulty; The level of mathematics required is quite basic; Software like @RISK can automate the tasks involved in simulation; The behaviour of and changes to the model can be investigated with great ease and speed.

How Monte-Carlo Simulation works?Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of valuesa probability distributionfor any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions. Depending upon the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. Monte Carlo simulation produces distributions of possible outcome values.By using probability distributions, variables can have different probabilities of different outcomes occurring. Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis. Common probability distributions include:Normal Or bell curve. The user simply defines the mean or expected value and a standard deviation to describe the variation about the mean. Values in the middle near the mean are most likely to occur. It is symmetric and describes many natural phenomena such as peoples heights. Examples of variables described by normal distributions include inflation rates and energy prices.Lognormal Values are positively skewed, not symmetric like a normal distribution. It is used to represent values that dont go below zero but have unlimited positive potential. Examples of variables described by lognormal distributions include real estate property values, stock prices, and oil reserves.Uniform All values have an equal chance of occurring, and the user simply defines the minimum and maximum. Examples of variables that could be uniformly distributed include manufacturing costs or future sales revenues for a new product.Triangular The user defines the minimum, most likely, and maximum values. Values around the most likely are more likely to occur. Variables that could be described by a triangular distribution include past sales history per unit of time and inventory levels.PERT-The user defines the minimum, most likely, and maximum values, just like the triangular distribution. Values around the most likely are more likely to occur. However values between the most likely and extremes are more likely to occur than the triangular; that is, the extremes are not as emphasized. An example of the use of a PERT distribution is to describe the duration of a task in a project management model.Discrete The user defines specific values that may occur and the likelihood of each. An example might be the results of a lawsuit: 20% chance of positive verdict, 30% change of negative verdict, 40% chance of settlement, and 10% chance of mistrial.During a Monte Carlo simulation, values are sampled at random from the input probability distributions. Each set of samples is called aniteration,and the resulting outcome from that sample is recorded. Monte Carlo simulation does this hundreds or thousands of times, and the result is a probability distribution of possible outcomes. In this way, Monte Carlo simulation provides a much more comprehensive view of what may happen. It tells you not only what could happen, but how likely it is to happen.

Monte Carlo simulation provides a number of advantages overdeterministic,or single-point estimate analysis: Probabilistic Results.Results show not only what could happen, but how likely each outcome is. Graphical Results.Because of the data a Monte Carlo simulation generates, its easy to create graphs of different outcomes and their chances of occurrence. This is important for communicating findings to other stakeholders. Sensitivity Analysis.With just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most. In Monte Carlo simulation, its easy to see which inputs had the biggest effect on bottom-line results. Scenario Analysis:In deterministic models, its very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios. Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. This is invaluable for pursuing further analysis. Correlation of Inputs.In Monte Carlo simulation, its possible to model interdependent relationships between input variables. Its important for accuracy to represent how, in reality, when some factors goes up, others go up or down accordingly.An enhancement to Monte Carlo simulation is the use of Latin Hypercube sampling, which samples more accurately from the entire range of distribution functionsA common misconception about Monte Carlo simulation is that the computational effort is combinatorial (exponential) in the number of uncertain inputs -- making it impractical for large models. This is true for simple discrete probability tree (or decision tree) methods. But, in fact, the great advantage of Monte Carlo is that the computation islinearin the number of uncertain inputs: It's proportional to the number of input distributions to be sampled.

COMPARISON OF SIMULATION TECHNIQUES:-SIMULATION METHOD

ANALYTICALMONTE-CARLO

Gives exact results(given the assumptions of the model) Very flexible. There is virtually no limit to the analysis. Empirical distributions can be handled

Once the model is developed, output will generally be rapidly obtained. Can generally be easily extended and developed as required.

It need not always be implemented on a computer paper analyses may suffice. Easily understood by non- mathematicians.

Generally requires restrictive assumptions to make the problem tractable. Usually requires a computer.

Because it is less flexible than Monte-Carlo. In particular, the scope for extending or developing a model may be limited. Calculations can take much longer than analytical models.

The model might only be understood by mathematicians. This may cause credibility problems if output conflicts with preconceived ideas of designers or management. Solutions are not exact, but depend on the number of repeated runs used to produce the output statistics. That is, all outputs are estimates.

RECOMMENDATIONS:- The worksheet approach consumes considerable system resources, and therefore may result in system lock-ups or other poor system performance when you need more than a few thousand iterations of the simulation. A single simulation can use multiple instances of the RAND () function, for example. Multiplying that single simulation by 20,000 iterations can cause the spreadsheet to bog down unbearably. For these situations in which the worksheet approach is inadequate, we turn to Excel's built-in programming language, Visual Basic for Applications (or "VBA"). Using the programming language we can write computer code which reads input from a worksheet and writes output back to the worksheet. Output can be in the form of printing a single output data row for each iteration (constrained by the same limits on number of rows described above), or the VBA code can tally the results itself and output only the final numbers (average, standard deviation, range, etc. of multiple variables) to designated spreadsheet cells.

CONCLUSION:-Simulation is truly an analytical tool with applications across the enterprise. Consider a capital investment situation such as the launch of a new product. Engineering is involved in the product design, during which simulation can be used to compare different design congurations without having to actually build the product. For example, automakers use computer simulation of cars to test design concepts. Operations is involved in facility design, supply chain conguration and management, manufacturing, and distribution. All of these areas lend themselves to simulation, which is probably the most common tool used in comparing different facility layouts and operating plans. Marketing is involved in the market development, distribution, sales, and customer support aspects of the new product. Since there is typically much un- certainty in the launch of a new product, simulation is a valuable tool for analysing the effects of uncertainties in market size, price sensitivities, and other factors. Of course, nance is involved in the overall nancial attractiveness of the launch of the new product, as well as in questions relating to the nancing of the capital investment. These decisions involve much uncertainty, and simulation is a tool to help evaluate the risk level of the project.

CHAPTER 6BIBLIOGRAPHY

BIBLIOGRAPHY:- BOOK: Research Methodology: Methods and Techniques BY: C.R.Kothari E-BOOKS, CASE PAPERS & JOURNALS:-1) Journal of Management and Marketing Research, Management information systems and business decision making: review, analysis, and recommendations BY: Srinivas Nowduri , Bloomsburg University of Pennsylvania LINK: http://www.aabri.com/manuscripts/10736.pdf2) Beyond Accuracy: How Models of Decision Making Compare to Human Decision Making, Master Thesis in Cognitive Science Lund University Sweden June 2005Author: Carl Christian Rolf LINK: http://fileadmin.cs.lth.se/cs/Personal/Carl_Christian_Rolf/ccr-msccog.pdf3) Simulation-based Optimization and Decision Making with Imperfect InformationBY: FARZAD KAMRANILINK: http://www.divaportal.org/smash/get/diva2:461227/FULLTEXT01.pdf4) Management Information System And Decision Making Process In EnterpriseBY: Predrag Ranisavljevi1, Tanja Spasi1, Ivana Mladenovi-Ranisavljevi2, high Business School Of Leskovac, University Of Ni, Faculty Of Technology Leskovac, SerbiaLINK: http://emit.kcbor.net/Emit%20clanci%20za%20sajt/EMIT%20Vol1%20No3/Management%20information%20system%20and%20decision%20making%20process%20in.pdf

5) The Role of MIS in Management Decision Making-Theoretical ApproachBY: Mihane Berisha-Namani(University of Pristina, Kosova)LINK: http://manager.faa.ro/download/561_1211.pdf

WEBSITES:-1) http://smallbusiness.chron.com/role-management-information-systems-decisionmaking-63454.html2) http://www.ijric.org/volumes/Vol5/1Vol5.pdf3) http://yourbusiness.azcentral.com/role-management-information-systems-decision-making-1826.html4) http://www.tutorialspoint.com/management_information_system/managerial_decision_making.htm5) http://notes.tyrocity.com/chapter-5-meaning-and-importance-of-decision-making/6) http://www.dodccrp.org/files/IC2J_v1n1_02_Moffat.pdf7) http://www.referenceforbusiness.com/management/Sc-Str/Simulation.html8) http://www.ignou.ac.in/upload/Unit-11-55.pdf9) https://www.uic.edu/classes/idsc/ids422/lect1.ppt10) http://www.gdrc.org/decision/dss-types.html11) https://onlinecampus.bu.edu/bbcswebdav/pid-843933-dt-content-rid-2221759_1/courses/13sprgmetad715_ol/module_03a/metad715_m03l02t02_managementinfosystems.html12) http://www.slideshare.net/manukumarkm/source-of-data-in-research13) http://adamowen.hubpages.com/hub/Understanding-The-Different-Types-of-Research-Data14) http://orsnz.org.nz/conf33/papers/p61.pdf15) http://www.ioz.pwr.wroc.pl/Pracownicy/Mielczarek/supp_c.pdf16) http://www.goldsim.com/Web/Introduction/WhentoSimulate/17) http://www.vault.com/industries-professions/industries/information-technology.aspx

18) http://www.academia.edu/232471/Decision-making_Theory_and_practice19) http://www.palisade.com/risk/monte_carlo_simulation.asp20) http://www.epixanalytics.com/modelassist/AtRisk/Model_Assist.htm#Montecarlo/How_Monte_Carlo_Simulation_Works.htm21) http://www.lumina.com/technology/monte-carlo-simulation-software/

MAEERS MAHARASHTRA INSTITUTE OF TECHNOLOGYDEPARTMENT OF MANAGEMENT SCIENCES AND RESEARCH

Dissertation Proposal Introduction of Topic:The topic which interests me for exploring as a research report will be Simulation Modelling for decision making. Decision making is an integral part of the functioning of any organization. To facilitate decision making in this ever-competitive world it is imperative that managers have the right information at the right time to bridge the gap between need and expectation.My aim would be to achieve a greater insight into the topic & also get enough information about the queries regarding the subject as to why decision making is important to organizations?, what are the benefits of MIS & DSS for decision making?, How Monte-Carlo Simulation method proves to be of greater advantage in making quick & accurate decisions? & also new techniques of using Simulation methods for greater speed & reliability in obtaining better results.

Research Objectives: I shall strive to achieve following objectives: To study different types of decision making structures used in organization To study the role of Information Systems in decision making To evaluate various Decision Support System (DSS) models & tools required for rational decision making. To gain more insight to the use of Simulation Modelling to a greater extent by the organizations. Research Methodology: It will be a desk research i.e. descriptive research which will explore the possibility of determining the effectiveness of Simulation Modelling in particular and also it will be purely based on Secondary Data collection so it will probably be subjected to the data already published in various research papers & websites so forth.

Expected Contribution of Study: I shall try to recommend certain new upcoming techniques of Simulation Methods which may be still unpracticed in organizations due to lack of infrastructure or knowledge & also will try to enlighten on my area of research, its advantages over other techniques as per my understanding & perception.

Chaitali Sanjeev Ghodke

This dissertation proposal is accepted & forwarded for approval Approved/Not Approved Dr. Mahesh AbaleProf. Poonam Rawat Professor & Head, MIT- DMSR