Wednesday, May 27, 2009

Chapter 9

Describe the decision-making process proposed by Simon.

Simon (1977) described the process as composed of three major phases: intelligence, design and choice. A fourth phase, implementation was added later.
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 than 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.

Why do managers need IT support?

It is difficult to make decisions without having the relevant information. Information is vital for each phase and activity in the decision-making process. Despite the widespread of information, making decisions is becoming increasingly difficult owing to the following trends:

- The number of alternatives to be considered is constantly increasing, due to innovation in technology, improved communications, the development of global markets, and the use of the internet and e-business. The more alternatives that exist, the more computer-assisted search and comparisons are needed.
- The decisions must be made under time pressure. Frequently, it is not possible to process information manually fast enough to be effective.
- 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.
- It is often necessary to access remote information rapidly, consult with expert, or conduct a group decision-making session, all without incurring large expenses. Decision makers can be different locations, as can the information. Bringing them all together quickly and inexpensively may be a difficult task.
These trends create major difficulties in decision-making. Fortunately a computerised analysis can be of enormous help.

Describe the decision matrix.

The three primary classes of problem structure and the three broad categories of the nature of decisions can be combined in a decision support matrix that consists of nine cells. Lower- level managers usually perform the structured and operational control oriented tasks (cells 1, 2 and 4). The tasks in cells 3, 5 and 7 are usually the responsibility of middle managers and professional staff. Finally, tasks in cells 6, 8, and 9 are generally the responsibility of senior executives.

Describe the capabilities of data mining.

Data mining can perform two basic operations: predicting trends and behaviours and identifying previously unknown patterns. Multidimensional view provides the users with a view of what is happening whereas data mining provides a view of why it is happening and what will happen in the future. In most cases the intent of data mining is to identify a business opportunity in order to create a sustainable competitive advantage.

Further capabilities of data mining include:
- Retailing and Sales: Predicting sales, preventing theft and fraud, and determining correct inventory levels and distribution schedules among outlets.
- 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 quality for) new loan offers.
- Manufacturing and Production: Predicting machinery failures and finding key factors that help optimize manufacturing capacity.
- 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.
- Policework: Tracking crime patterns, locations, and criminal behaviour; identifying attributes to assist in solving criminal cases.
-Health care: Correlating the demographics of patients with critical illness and development better insights on how to identify and treat symptoms and their causes.
-Marketing: Classifying customer demographics that can be used to predict which customers will respond to a mailing or buy a particular product.

What are some of the capabilities of digital dashboards?

The capabilities if digital dashboard include:
- Drill-down: the ability to go to details, at several levels; can be done by a series of menus or by directed queries (using intelligent agents and natural language processing).
- Critical success factors (CSFs): the factors most critical for the success of business, they may be organizational, industry, department, etc.
- Key performance indicators (KPIs): The specific measures of CSFs.
- Status access: The latest data available on KPI or some other metric, ideally in real time.
- Trend Analysis: Short- medium and long-term trend of KPIs or metrics, which are projected using forecasting methods.
- Adhoc analysis: Analyses made anytime, upon demands and with any desired factors and relationships.
- Exception Reporting: Reports the highlight deviations larger than certain threshold. Reports may include only deviations.

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