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Chapter 9

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Department
Administrative Studies
Course
ADMS 2511
Professor
Donna Rex
Semester
Summer

Description
Chapter 9: Explain the impact of current trends that create major difficulties in decision making  It is difficult to make good decisions without valid and relevant information. Information is vital for each phase and activity in the decision-making process. Despite the widespread availability of information, making decisions is becoming increasingly difficult due to the following trends: o The number of alternatives to be considered is constantly increasing, due to innovations in technology, improved communications, the development of global markets, and the use of the Internet and e-business.  A key to good decision making is to explore and compare many relevant alternatives. The more alternatives that exist, the more computer-assisted search and comparisons that are needed. o Typically, decisions must be made under time pressure.  Frequently it is not possible to process information manually fast enough to be effective. o Due to increased uncertainty in the decision environment, decisions are becoming more complex. It is usually necessary to conduct a sophisticated analysis in order to make a good decision. Such analysis requires the use of modelling. o It is often necessary to rapidly access remote information, consult with experts, or conduct a group decision- making session, all without incurring large expenses.  Decision makers can be in different locations, as can the information. Bringing them all together quickly and inexpensively may be a difficult task. Describe the purpose of each of the four phases of the systematic process of decision making (Simon, 1977), and explain how information systems could help perform the tasks in each of the phases of Simon’s decision model  A decision refers to a choice that individuals and groups make among two or more alternatives.  Decisions are diverse and are made continuously. Decision making is a systematic process. Simon (1977) described the process as composed of three major phases: o intelligence, o design, and o choice. o A fourth phase, implementation, was added later.  Figure 9.1 illustrates this four-stage process, indicating which tasks are included in each phase.  Note that there is a continuous flow of information from intelligence to design to choice (bold lines), but at any phase there may be a return to a previous phase (broken lines).  The decision-making process starts with the intelligence phase, in which managers examine a situation and identify and define the problem.  In the design phase, decision makers construct a model that simplifies the problem. They do this by making assumptions that simplify reality and by expressing the relationships among all the relevant variables. Managers then validate the model by using test data. Finally, decision makers set criteria for evaluating all potential solutions that are proposed.  The choice phase involves selecting a solution, which is tested “on paper.” Once this proposed solution seems to be feasible, decision making enters the last phase—implementation.  Implementation is successful if the proposed solution actually resolves the problem. Failure leads to a return to the previous phases. Computer-based decision support attempts to automate several tasks in the decision-making process, in which modelling is the core. Using the decision support matrix, identify and describe the three categories of problem structure and three broad categories that represent the nature of decisions  PROBLEM STRUCTURE o The first dimension is problem structure, where decision-making processes fall along a continuum ranging from highly structured to highly unstructured decisions.  Structured decisions refer to routine and repetitive problems for which standard solutions exist, such as inventory control. In a structured problem, the first three of the decision process phases—intelligence, design, and choice—are laid out in a particular sequence, and the procedures for obtaining the best (or at least a good enough) solution are known. Two basic criteria that are used to evaluate proposed solutions are minimizing costs and maximizing profits. o At the other extreme of problem complexity are unstructured decisions. These are “fuzzy,” complex problems for which there are no cut-and-dried solutions.  An unstructured problem is one in which intelligence, design, and choice are not organized in a particular sequence. In such a problem, human intuition often plays an important role in making the decision. Typical unstructured problems include planning new service offerings, hiring an executive, and choosing a set of research and development (R&D) projects for the coming year. o Located between structured and unstructured problems are semistructured problems, in which only some of the decision process phases are structured.  Semistructured problems require a combination of standard solution procedures and individual judgment. Examples of semistructured problems are evaluating employees, setting marketing budgets for consumer products, performing capital acquisition analysis, and trading bonds.  THE NATURE OF DECISIONS o The second dimension of decision support deals with the nature of decisions. We can define three broad categories that encompass all managerial decisions:  Operational control—executing specific tasks efficiently and effectively  Management control—acquiring and using resources efficiently in accomplishing organizational goals  Strategic planning—the long-range goals and policies for growth and resource allocation Describe the purpose of Business Intelligence systems and of the two types of systems that are referred to as Business Intelligence systems  Once an organization has captured data and organized it into databases, data warehouses, and data marts, it can use it for further analysis.  Business Intelligence Systems (BI) refers to applications and technologies for consolidating, analyzing, and providing access to vast amounts of data to help users make better business and strategic decisions.  Business intelligence systems encompass two types of information systems: o (1) those that provide data analysis tools (that is, multidimensional data analysis or online analytical processing, data mining, and decision support systems) and o (2) those that provide easily accessible information in a structured format (that is, digital dashboards). Explain the purpose, method and tools used to perform Multidimensional Data Analysis  Multidimensional analysis provides users with an excellent view of what is happening or what has happened. o To accomplish this, multidimensional analysis tools allow users to “slice and dice” the data in any desired way. o In the data warehouse, relational tables can be linked, forming multidimensional data structures, or cubes. o This process looks like rotating the cube as users view it from different perspectives. o Statistical tools provide users with mathematical models that can be applied to the data to gain answers to their queries.  Example o Assume that a business has organized its sales force by regions—say Eastern, Western, and Central. These three regions might then be broken down into provinces. The VP of sales could slice and dice the data cube to see the sales figures for each region (that is, the sales of nuts, screws, bolts, and washers). The VP might then want to see the Eastern region broken down by province so he could evaluate the performance of individual provincial sales managers. Note that the business organization is reflected in the multidimensional data structure.  The power of multidimensional analysis lies in its ability to analyze the data in such a way as to allow users to quickly answer business questions. o “How many bolts were sold in the Eastern region in 2006?” o “What is the trend in sales of washers in the Western region over the past three years?” o “Are any of the four products typically purchased together?” IT's About Identify the two basic operations that can be performed using Data mining and be able to provide examples of their application in a business case  Data Mining derives its name from searching for valuable business information in a large database, data warehouse, or data mart. Data mining can perform two basic operations: (1) predicting trends and behaviours and (2) identifying previously unknown patterns.  We emphasize that multidimensional analysis provides users with a view of what is happening. Data mining addresses why it is happening and provides predictions of what will happen in the future. o 1. first operation, data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered directly and quickly from the data.  A typical example of a predictive problem is targeted marketing. Data mining can use data from past promotional mailings to identify people who are most likely to respond favourably to future mailings. Another example of a predictive problem is forecasting bankruptcy and other forms of default. o 2. Data mining can also identify previously hidden patterns in a single step.  example, it can analyze retail sales data to discover seemingly unrelated products that are often purchased together.  One interesting pattern-discovery problem is detecting fraudulent credit card transactions. After you use your credit card for a time, a pattern emerges of the typical ways you use your card (for example, where use your card, the amount you spend, and so on). If your card is stolen and used fraudulently, this usage is often different than your pattern of use. Data mining tools can distinguish the difference in the two patterns of use and bring this issue to your attention.  Numerous data mining applications are used in business and in other fields. o Retailing and sales Predicting sales, preventing theft and fraud, and determining correct inventory levels and distribution schedules among outlets. o Banking Forecasting levels of bad loans and fraudulent credit card use, predicting credit card spending by new customers, and determining which kinds of customers will best respond to (and qualify for) new loan offers. o Manufacturing and production Predicting machinery failures, and finding key factors that help optimize manufacturing capacity. o Insurance Forecasting claim amounts and medical coverage costs, classifying the most important elements that affect medical coverage, and predicting which customers will buy new insurance policies. o Police work Tracking crime patterns, locations, and criminal behaviour; identifying attributes to assist in solving criminal cases. o Health care Correlating patients' demographics with critical illnesses, and developing better insights on how to identify and treat symptoms and their causes. o Marketing Classifying customer demographics that can be used to predict which customers will respond to a mailing or buy a particular product. Explain how decision support systems combine models and data to support decision making  Decision support systems (DSS) combine models and data in an attempt to solve semistructured and some unstructured problems with extensive user involvement. o Models are simplified representations, or abstractions, of reality. o DSSs are designed to enable interactive access to data, to enable manipulation of this data, and to provide business managers and analysts the ability to conduct appropriate analyses. o Decision support systems can manipulate data, enhance learning, and contribute to all levels of decision making. o DSSs also employ mathematical models. o Finally, they have the related capabilities of sensitivity analysis, what-if analysis, and goal-seeking analysis. Describe the three types of analysis that can be performed by decision support systems and be able to provide examples within a business case  SENSITIVITY ANALYSIS o is the study of the impact that changes in one (or more) parts of a decision-making model have on other parts. o Most sensitivity analyses examine the impact that changes in input variables have on output variables. o Sensitivity analysis is extremely valuable because it enables the system to adapt to changing conditions and to the varying requirements of different decision-making situations. o It provides a better understanding of the model and the problem it purports to describe. It may also increase the users' confidence in the model, especially if it indicates that the model is not very sensitive to changes.  A sensitive model means that small changes in conditions dictate a different solution.  In a nonsensitive model, changes in conditions do not significantly change the recommended solution.  For this reason the chances for a solution to succeed are much higher in a nonsensitive model than in a sensitive one.  WHAT-IF ANALYSIS o A model builder must make predictions and assumptions regarding the input data, much of which is based on the assessment of uncertain futures. results depend on the accuracy of these assumptions which can be subjective. o What if Analysis attempts to predict the impact of a change in the assumptions (input data) on the proposed solution.  For example, what will happen to the total inventory cost if the originally assumed cost of carrying inventories is not 10 percent but 12 percent? In a well-designed BI system, managers themselves can interactively ask the computer these types of questions as many times as they need to.  GOAL-SEEKING ANALYSIS o represents a “backward” solution approach. o It attempts to find the value of the inputs necessary to achieve a desired level of output.  For example, let's say that an initial solution of a BI system yielded a profit of $2 million. Management
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