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SIM - Mc leod ch11

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  • 1. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 1 Management Information Systems, 10/e Raymond McLeod and George Schell
  • 2. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 2 Chapter 11 Decision Support Systems
  • 3. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 3 Learning Objectives ►Understand the fundamentals of decision making & problem solving. ►Know how the decision support system (DSS) concept originated. ►Know the fundamentals of mathematical modeling. ►Know how to use an electronic spreadsheet as a mathematical model.
  • 4. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 4 Learning Objectives (Cont’d) ►Be familiar with how artificial intelligence emerged as a computer application & know its main areas. ►Know the four basic parts of an expert system. ►Know what a group decision support system (GDSS) is & the different environmental settings that can be used.
  • 5. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 5 Problem-Solving & Decision Making Review ►Problem solving consists of response to things going well & also to things going badly. ►Problem is a condition or event that is harmful or potentially harmful to a firm or that is beneficial or potentially beneficial. ►Decision making is the act of selecting from alternative problem solutions. ►Decision is a selected course of action.
  • 6. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 6 Problem-Solving Phases ►Herbert A. Simon’s four basic phases:  Intelligence phase – Searching the environment for conditions calling for a solution.  Design activity – inventing, developing, & analyzing possible course of actions.  Choice activity – Selecting a particular course of action from those available.  Review activity – Assessing past choices.
  • 7. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 7 Frameworks & Systems Approach ►Problem-solving frameworks  General systems model of the firm.  Eight-element environmental model. ►Systems approach to problem- solving, involves a series of steps grouped into three phases – preparation effort, definition effort, & solution effort.
  • 8. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 8 Importance of Systems View ► Systems view which regards business operations as systems embedded within a larger environmental setting; abstract way of thinking; potential value to the manager.  Prevents the manager from getting lost in the complexity of the organizational structure & details of the job.  Recognizes the necessity of having good objectives.  Emphasizes the importance of all of the parts of the organization working together.  Acknowledges the interconnections of the organization with its environment.  Places a high value on feedback information that can only be achieved by means of a closed-loop system.
  • 9. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 9 Building on the Concepts ► Elements of a problem-solving phase.  Desired state – what the system should achieve.  Current state – what the system is now achieving.  Solution criterion – difference between the current state & the desired state. ► Constraints.  Internal take the form of limited resources that exist within the firm.  Environmental take the form of pressures from various environmental elements that restrict the flow of resources into & out of the firm. ► When all of these elements exist & the manager understands them, a solution to the problem is possible!
  • 10. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 10 Figure 11.1 Elements of the Problem-Solving Process
  • 11. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 11 Selecting the Best Solution ►Henry Mintzberg, management theorist, has identified three approaches: ►Analysis – a systematic evaluation of options. ►Judgment – the mental process of a single manager. ►Bargaining – negotiations between several managers.
  • 12. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 12 Problem vs. Symptoms ► Symptom is a condition produced by the problem. ► Structured problem consists of elements & relationships between elements, all of which are understood by the problem solver. ► Unstructured problem is one that contains no elements or relationships between elements that are understood by the problem solver. ► Semistructured problem is one that contains some elements or relationships that are understood by the problem solver & some that are not.
  • 13. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 13 Types of Decisions ► Programmed decisions are “repetitive & routine, to the extent that a definite procedure has been worked out for handling them so that they don’t have to be treated de novo (as new) each time they occur. ► Nonprogrammed decisions are “novel, unstructured, & unusually consequential. There’s no cut-and-dried method for handling the problem because its precise nature & structure are elusive or complex, because it is so important that it deserves a custom-tailored treatment”.
  • 14. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 14 Decision Support Systems ► Gorry & Scott Morton (1971) argued that an information system that focused on single problems faced by single managers would provide better support. ► Central to their concept was a table, called the Gorry-Scott Morton grid (Figure 11.2) that classifies problems in terms of problem structure & management level. ► The top level is called the strategic planning level, the middle level - the management control level, & the lower level - the operational control level. ► Gorry & Scott Morton also used the term decision support system (DSS) to describe the systems that could provide the needed support.
  • 15. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 15 Figure 11.2 The Gorry & Scott- Morton Grid
  • 16. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 16 A DSS Model ► Originally the DSS was conceived to produce periodic & special reports (responses to database queries), & outputs from mathematical models. ► An ability was added to permit problem solvers to work in groups. ► The addition of groupware enabled the system to function as a group decision support system (GDSS). ► Figure 11.3 is a model of a DSS. The arrow at the bottom indicates how the configuration has expanded over time. ► More recently, artificial intelligence (AI) capability has been added, along with an ability to engage in online analytical programming (OLAP).
  • 17. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 17 Figure 11.3 DSS Model that Incorporates GDSS, OLAP, & AI
  • 18. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 18 Mathematical Modeling ► Model is an abstraction of something. It represents some object or activity, which is called an entity. ► There are four basic types of models:  Physical model is a three-dimensional representation of its entity.  Narrative model, which describes its entity with spoken or written words.  Graphic model represents its entity with an abstraction of lines, symbols, or shapes (Figure 11.4). ►Economic order quantity (EOQ) is the optimum quantity of replenishment stock to order from a supplier.  Mathematical model is any mathematical formula or equation.
  • 19. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 19 Formula to Compute Economic Order Quantity (EOQ)
  • 20. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 20 Figure 11.4 Graphical Model of EOQ
  • 21. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 21 Uses of Models ► Facilitate Understanding: Once a simple model is understood, it can gradually be made more complex so as to more accurately represent its entity. ► Facilitate Communication: All four types of models can communicate information quickly and accurately. ► Predict the Future: The mathematical model can predict what might happen in the future but a manager must use judgment & intuition in evaluating the output. ► A mathematical model can be classified in terms of three dimensions: the influence of time, the degree of certainty, & the ability to achieve optimization.
  • 22. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 22 Classes of Mathematical Models ► Static model doesn’t include time as a variable but deals only with a particular point in time. ► Dynamic model includes time as a variable; it represents the behavior of the entity over time. ► Probabilistic model includes probabilities. Otherwise, it is a deterministic model.  Probability is the chance that something will happen. ► Optimizing model is one that selects the best solution among the alternatives. ► Suboptimizing model (satisficing model) does not identify the decisions that will produce the best outcome but leaves that task to the manager.
  • 23. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 23 Simulation ► The act of using a model is called simulation while the term scenario is used to describe the conditions that influence a simulation. ► For example, if you are simulating an inventory system, as shown in Figure 11.5, the scenario specifies the beginning balance & the daily sales units. ► Models can be designed so that the scenario data elements are variables, thus enabling different values to be assigned. ► The input values the manager enters to gauge their impact on the entity are known as decision variables. ► Figure 11.5 gives an example of decision variables such as order quantity, reorder point, & lead time.
  • 24. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 24 Figure 11.5 Scenario Data & Decision Variables from a Simulation
  • 25. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 25 Simulation Technique & Format of Simulation Output ► The manager usually executes an optimizing model only a single time. ► Suboptimizing models, however, are run over & over, in a search for the combination of decision variables that produces a satisfying outcome (known as playing the what-if game). ► Each time the model is run, only one decision variable should be changed, so its influence can be seen. ► This way, the problem solver systematically discovers the combination of decisions leading to a desirable solution.
  • 26. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 26 A Modeling Example ► A firm’s executives may use a math model to assist in making key decisions & to simulate the effect of: 1.Price of the product; 2.Amount of plant investment; 3.Amount to be invested in marketing activity; 4.Amount to be invested in R & D. ► Furthermore, executives want to simulate 4 quarters of activity & produce 2 reports: an operating statement & an income statement. ► Figures 11.6 and 11.7 shows the input screen used to enter the scenario data elements for the prior quarter & next quarter, respectively.
  • 27. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 27 Figure 11.6 Model Input Screen for Entering Scenario Data for Prior
  • 28. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 28 Figure 11.7 Model Input Screen for Entering Scenario Data for Next
  • 29. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 29 Model Output ► The next quarter’s activity (Quarter 1) is simulated, & the after-tax profit is displayed on the screen. ► The executives then study the figure & decide on the set of decisions to be used in Quarter 2. These decisions are entered & the simulation is repeated. ► This process continues until all four quarters have been simulated. At this point the screen has the appearance shown in Figure 11.8. ► The operating statement in Figure 11.9 & the income statement in Figure 11.10 are displayed on separate screens.
  • 30. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 30 Figure 11.8 Summary Output from the Model
  • 31. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 31 Figure 11.9 Operating Statement Shows Nonmonetary Results
  • 32. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 32 Figure 11.10 Income Statement Shows Nonmonetary Results
  • 33. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 33 Modeling Advantages & Disadvantages ► Advantages:  The modeling process is a learning experience.  The speed of the simulation process enables the consideration of a larger number of alternatives.  Models provide a predictive power - a look into the future - that no other information-producing method offers.  Models are less expensive than the trial-and-error method. ► Disadvantages:  The difficulty of modeling a business system will produce a model that does not capture all of the influences on the entity.  A high degree of mathematical skill is required to develop & properly interpret the output of complex models.
  • 34. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 34 Mathematical Modeling Using Electronic Spreadsheets ► The technological breakthrough that enabled problem solvers to develop their own math models was the electronic spreadsheet. ► Static model: Figure 11.11 shows an operating budget in column form. The columns are for: the budgeted expenses, actual expenses, & variance, while rows are used for the various expense items. ► A spreadsheet is especially well-suited for use as a dynamic model. The columns are excellent for the time periods, as illustrated in Figure 11.12. ► A spreadsheet also lends itself to playing the “what-if” game, where the problem solver manipulates 1 or more variables to see the effect on the outcome of the simulation.
  • 35. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 35 Figure 11.11 Spreadsheet Rows & Columns Provide Format for Columnar Report
  • 36. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 36 Figure 11.12 Spreadsheet Columns are Excellent for Time Periods in Dynamic Model
  • 37. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 37 Spreadsheet Model Interface ► When using a spreadsheet as a mathematical model, the user can enter data or make changes directly to the spreadsheet cells, or by using a GUI ► The pricing model described earlier in Figures 11.6-11.10 could have been developed using a spreadsheet, and had the graphical user interface added ► The interface could be created using a programming language such as Visual Basic and would likely require an information specialist to develop ► A development approach would be for the user to develop the spreadsheet and then have the interface added by an information specialist.
  • 38. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 38 Artificial Intelligence ► Artificial intelligence (AI) is the activity of providing such machines as computers with the ability to display behavior that would be regarded as intelligent if it were observed in humans. ► AI is being applied in business in knowledge-based systems, which use human knowledge to solve problems. ► The most popular type of knowledge-based system are expert systems, which are computer programs that try to represent the knowledge of human experts in the form of heuristics. ► These heuristics allow an expert system to consult on how to solve a problem: called a consultation - the user consults the expert system for advice.
  • 39. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 39 Areas of AI ► Expert system is a computer program that attempts to represent the knowledge of human experts in the form of heuristics. ► Heuristic is a rule of thumb or a rule of good guessing. ► Consultation is the act of using an expert system. ► Knowledge engineer has special expertise in artificial intelligence; adept in obtaining knowledge from the expert.
  • 40. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 40 Areas of AI (Cont’d) ►Neural networks mimic the physiology of the human brain. ►Genetic algorithms apply the “survival of the fittest” process to enable problem solvers to produce increasingly better problem solutions. ►Intelligent agents are used to perform repetitive computer-related tasks; i.e. data mining.
  • 41. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 41 Expert System Configuration ► User interface enables the manager to enter instructions & information into the expert system & to receive information from it. ► Knowledge base contains both facts that describe the problem area & knowledge representation techniques that describe how the facts fit together in a logical manner. ► Problem domain is used to describe the problem area.
  • 42. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 42 Expert System Configuration (Cont’d) ► Rule specifies what to do in a given situation & consists of two parts:  A condition that may or may not be true, and  An action to be taken when the condition is true. ► Inference engine is the portion of the expert system that performs reasoning by using the contents of the knowledge base in a particular sequence. ► Goal variable is assigning a value to the problem solution.
  • 43. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 43 Expert System Configuration (Cont’d) ► Expert system shell is a ready-made processor that can be tailored to a specific problem domain through the addition of the appropriate knowledge base. ► Case-based reasoning (CBR) uses historical data as the basis for identifying problems & recommending solutions. ► Decision tree is a network-like structure that enables the user to progress from the root through the network of branches by answering questions relating to the problem.
  • 44. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 44 Figure 11.13 Expert System Model
  • 45. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 45 Group Decision Support System ► Group decision support system (GDSS) is “a computer-based system that supports groups of people engaged in a common task (or goal) & that provides an interface to a shared environment”. ► Aliases group support system (GSS), computer- supported cooperative work (CSCW), computerized collaborative work support, & electronic meeting system (EMS). ► Groupware the software used in these settings. ► Improved communications make possible improved decisions.
  • 46. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 46 GDSS Environmental Settings ► Synchronous exchange when members meet at the same time. ► Asynchronous exchange when members meet at different times. ► Decision room is the setting for small groups of people meeting face-to-face. ► Facilitator is the person whose chief task is to keep the discussion on track. ► Parallel communication is when all participants enter comments at the same time,& ► Anonymity is when nobody is able to tell who entered a particular comment; participants say what they REALLY think without fear.
  • 47. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 47 Figure 11.14 Group Size & Location Determine DSS Environmental Settings
  • 48. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell 48 GDSS Environmental Settings (Cont’d) ► Local area decision network when it is impossible for small groups of people to meet face-to-face, the members can interact by means of a local area network, or LAN. ► Legislative session when the group is too large for a decision room.  Imposes certain constraints on communications such as equal participation by each member is removed or less time is available. ► Computer-mediated conference several virtual office applications permit communication between large groups with geographically dispersed members.  Teleconferencing applications include computer conferencing, audio conferencing, & videoconferencing.