Management Information Systems, 10/e Raymond McLeod and George Schell © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Decision Support Systems Chapter 11 Decision Support Systems © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell 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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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! © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.1 Elements of the Problem-Solving Process © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell 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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell 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”. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.2 The Gorry & Scott-Morton Grid © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell 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). © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.3 DSS Model that Incorporates GDSS, OLAP, & AI © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Formula to Compute Economic Order Quantity (EOQ) © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.4 Graphical Model of EOQ © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell 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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell 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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.5 Scenario Data & Decision Variables from a Simulation © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell 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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.6 Model Input Screen for Entering Scenario Data for Prior © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.7 Model Input Screen for Entering Scenario Data for Next © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell 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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.8 Summary Output from the Model © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.9 Operating Statement Shows Nonmonetary Results © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.10 Income Statement Shows Nonmonetary Results © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Modeling Advantages & Disadvantages 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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell Figure 11.11 Spreadsheet Rows & Columns Provide Format for Columnar Report © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell Figure 11.12 Spreadsheet Columns are Excellent for Time Periods in Dynamic Model © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell 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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell 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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Figure 11.13 Expert System Model © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

Management Information Systems, 10/e Raymond McLeod and George Schell Figure 11.14 Group Size & Location Determine DSS Environmental Settings © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell

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. © 2007 by Prentice Hall Management Information Systems, 10/e Raymond McLeod and George Schell