Management Information Systems, 10/e

1. Management Information Systems, 10/e Raymond McLeod and George Schell Management Information Systems, 10/e Raymond McLeod and George Schell

2. Chapter 11 Decision Support Systems 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. 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. 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. 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. 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. 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. 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! Management Information Systems, 10/e Raymond McLeod and George Schell

10. Figure 11.1 Elements of the Problem-Solving Process 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. 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. 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”. 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. Management Information Systems, 10/e Raymond McLeod and George Schell

15. Figure 11.2 The Gorry & Scott-Morton Grid 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). Management Information Systems, 10/e Raymond McLeod and George Schell

17. Figure 11.3 DSS Model that Incorporates GDSS, OLAP, & AI 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. Management Information Systems, 10/e Raymond McLeod and George Schell

19. Formula to Compute Economic Order Quantity (EOQ) Management Information Systems, 10/e Raymond McLeod and George Schell

20. Figure 11.4 Graphical Model of EOQ 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. 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. • Dynamicmodel includes time as a variable;it represents the behavior of the entity over time. • Probabilistic model includes probabilities. Otherwise, it is a deterministicmodel. • Probability is the chance that something will happen. • Optimizing model is one that selects the best solution among the alternatives. • Suboptimizingmodel (satisficing model) does not identify the decisions that will produce the best outcome but leaves that task to the manager. 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. Management Information Systems, 10/e Raymond McLeod and George Schell

24. Figure 11.5 Scenario Data & Decision Variables from a Simulation 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. 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: • Price of the product; • Amount of plant investment; • Amount to be invested in marketing activity; • 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. Management Information Systems, 10/e Raymond McLeod and George Schell

27. Figure 11.6 Model Input Screen for Entering Scenario Data for Prior Management Information Systems, 10/e Raymond McLeod and George Schell

28. Figure 11.7 Model Input Screen for Entering Scenario Data for Next 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. Management Information Systems, 10/e Raymond McLeod and George Schell

30. Figure 11.8 Summary Output from the Model Management Information Systems, 10/e Raymond McLeod and George Schell

31. Figure 11.9 Operating Statement Shows Nonmonetary Results Management Information Systems, 10/e Raymond McLeod and George Schell

32. Figure 11.10 Income Statement Shows Nonmonetary Results 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. 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. Management Information Systems, 10/e Raymond McLeod and George Schell

35. Figure 11.11 Spreadsheet Rows & Columns Provide Format for Columnar Report Management Information Systems, 10/e Raymond McLeod and George Schell

36. Figure 11.12 Spreadsheet Columns are Excellent for Time Periods in Dynamic Model 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. 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. 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. 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. 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. 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. 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. Management Information Systems, 10/e Raymond McLeod and George Schell

44. Figure 11.13 Expert System Model 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 worksupport, & electronic meeting system (EMS). • Groupware the software used in these settings. • Improved communications make possible improved decisions. 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. Management Information Systems, 10/e Raymond McLeod and George Schell

47. Figure 11.14 Group Size & Location Determine DSS Environmental Settings 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 applicationsinclude computer conferencing, audio conferencing, & videoconferencing. Management Information Systems, 10/e Raymond McLeod and George Schell