Decision-Making: Introduction and Definitions The opening vignette demonstrated some aspects of a typical business decision: The decision is often made by a group. Group members may have biases. Empowering a group leads to better decisions. Individuals may also be responsible for making a decision. There may be many (hundreds or even thousands) of alternatives to consider. The result of making a business decision usually materializes in the future. No one is a perfect predictor of the future, especially in the long run. Decisions are interrelated. A specific decision may affect many individuals and groups within the organizational system. Decision making involves a process of thinking about the problem leading to the need for data and modeling of the problem (loosely speaking: understanding the relationships among its different aspects). This leads to interpretation and application of knowledge. Feedback is an important aspect of decision-making.

Additionally, Groupthink (buy-in by group members without any thinking) can lead to bad decisions. There can be several, conflicting objectives. Many decisions involve risk. Different people have different attitudes towards risk. Decision-makers are interested in evaluating what-if scenarios. Experimentation with the real system (i.e., develop a schedule, try it. And see how well it works – trial and error) may result in failure. Experimentation with the real system is possible only for one set of conditions at a time and can be disastrous. Changes in the decision-making environment may occur continuously, leading to invalidating assumptions about the situation (e.g., deliveries around holiday times may increase, requiring a different view of the problem). Changes in the decision-making environment may affect decision quality by imposing time pressure on the decision-maker. Collecting information and analyzing a problem takes time and can be expensive. It is difficult to determine when to stop and make a decision. There may not be sufficient information to make an intelligent decision. There may be too much information available (information overload).

Decision-Making Disciplines Behavioral disciplines include Anthropology Law Philosophy Political science Psychology Social psychology Sociology. Scientific disciplines include Computer science Decision analysis Economics Engineering Hard sciences: biology, chemistry, physics, etc. Management science/operations research Mathematics Statistics.

The System and its Environment Stockholders Vendors Customers Government Banks Competitors System boundary Environment Raw materials Costs Resources Procedures Programs Tools Activities Decisions Performances Consequences Finished products Services delivered InputsProcessesOutputs Decision maker Feedback

Models Iconic (Scale) Models Analog Models Mathematical (Quantitative) Models

The Benefits of Models Model manipulation (changing decision variables or the environment) is much easier than manipulating the real system. Experimentation is easier and does not interfere with the daily operation of the organization. Models enable the compression of time. Years of operations can be simulated in minutes or seconds of computer time. The cost of modeling analysis is much less than the cost of a similar experience conducted on a real system. The cost of making mistakes during a trial-and-error experiment is much less when models are used rather than real systems. The business environment involves considerable uncertainty. With modeling, a manager can estimate the risks resulting from specific actions. Mathematical models enable the analysis of a very large, sometimes infinite, number of possible solutions. Even in simple problems, managers often have a large number of alternatives from which to choose. Models enhance and reinforce learning and training. Models and solution methods are readily available over the Web. There are many Java applets (and other Web programs) that readily solve models. A management-support system uses models for the following reasons:

The Decision Making/Modeling Process Intelligence Phase Design Phase Choice Phase Implementation Of Solution Organizational Objectives Search and Scanning Procedures Data Collection Problem Identification Problem Ownership Problem Classification Problem Statement Formulate a Model Set Criteria for Choice Search for Alternatives Predict and Measure Outcomes Solution to the Model Sensitivity Analysis Selection of Best (Good) alternative(s) Plan for Implementation (Action) Failure Verification, testing of proposed Solution Validation of the Model Simplification Assumptions Success Alternatives Solution Problem statement

DSS Support ANN MIS Data Mining, OLAP EIS ERP GSS EIS SCM CRM ERP KMS Management Science ANN GSS EIS KMS ERP Intelligence Design Choice Implementation Phases DSS ES CRM SCM

The Seven Deadly Sins of Decision-Making 1. Believing that you already have all the answers. 2. Asking the wrong questions. 3. The old demon ego. 4. Flying-by-the-seat-of-your-pants saves money—doesn’t it. 5. All aboard the bandwagon: if it works for them, it’ll work for us. 6. Hear no evil. 7. Hurry up and wait: making no decision can be the same as making a bad decision.