Ch. 3
Analyzing Business Decision Processes

Decisions can be categorized as routine and recurring decisions or programmed decisions with set responses, and as non-routine or infrequent decisions that are usually less structured. Examples of routine decisions that can be automated and programmed include placing an order to replenish inventory, sending delinquency notices, or routing trains. Non-routine decisions that can benefit from decision support include deciding on a new supplier for a part, disciplining an employee who is constantly late for work, or creating a budget.

Managers should not treat routine decisions as if they were non-routine. If a decision is "generic" and routine, valuable time and resources should not be expended each time the decision occurs as one would with a non-routine, non-recurring decision. Recurring decision situations should be analyzed and "programmed" as much as is possible and they should be supported when possible by technology. The potential rewards from improving routine, recurring decisions are very large.

What situations are less likely to benefit from computerized decision aids and decision support? One situation that comes rapidly to mind is one of limited consequence, e.g. low return, and few positive or negative consequences, such as assigning parking spaces. Another is a situation where political factors outweigh or gain ascendancy over facts and analysis. In general, computerized decision aids support rational decision behavior that uses analytical decision processes. Where the situation does not require, expect, encourage or need analysis and intended rationality using a computer support system with models, databases and other sophisticated tools will be unnecessary and may be manipulated or distorted. Rather than dwell on when decision analysts should avoid suggesting DSS, it seems more important to help analysts identify "good situations" for building Decision Support Systems.

Computerized decision support should be considered when managers are in decision situations characterized by one or more of the following factors: complexity, uncertainty, multiple groups with a stake in the decision outcome (multiple stakeholders), a large amount of information (especially company data), and/or rapid change in information. Complex decision situations with many variables, complex causal relationships and an available historical database can sometimes be modeled. These are complex situations and models can simplify such decision situations, aid in understanding them and help test alternatives. Computerized models, especially visual models, can be very useful in these situations. The model is a representation of the actual situation and analyses performed using the model can help the decision maker(s) anticipate consequences of alternatives. Sometimes a software model can actually recommend optimal choices to a decision-maker.

Risk and uncertainty characterize many decision situations. Managers in these situations need to assess risks and in some cases they need to assess the financial consequences of acting in an uncertain or risky situation. Computerized tools can help elicit and apply risk information in a decision situation. Computerized support systems can also help deal with large amounts of information and rapidly changing information. Finally, in some situations many people need to be involved and consulted. Enterprise-wide and Group Decision Support Systems (GDSS) can help involve multiple stakeholders, especially those internal to the organization.