Decision Support Systems: Foundations and Variations

Over the past quarter century, economic and technological forces have produced radical redefinitions of work, the workplace, and the marketplace. They have ushered in the era of knowledge workers, knowledge-based organizations, and the knowledge economy. People have always used the knowledge available to them to make decisions that shape the world in which they live.

Decisions of workers, consumers, and organizations range from those affecting the world in some small or fleeting way to those of global and lasting proportions. In recent times, the number of decisions being made per time period and the complexity of factors involved in decision activities have grown dramatically. As the world’s supply of knowledge continues to accelerate, the amount of knowledge used in making decisions has exploded.

Computer-based systems that help decision makers deal with both the knowledge explosion and the incessant demands for decisions in a fast-paced, complicated world are called decision support systems (DSSs). Such systems have become practically indispensable for high performance, competitiveness, and even organizational survival.

Imagine an organization in which managers and other workers cannot use computers to aid any of their decisional activities. Contrast this fantasy with the vision of an organization whose managers and other knowledge workers routinely employ computers to get at and process knowledge that has a bearing on decisions being made.

These DSSs store and process certain kinds of knowledge in much higher volumes and at much higher speeds than the human mind. In addition to such efficiency advantages, they can also be more effective for certain kinds of knowledge handling because they are not subject to such common human conditions as oversight, forgetfulness, miscalculation, bias, and stress. Failure to appreciate or exploit such decision support capabilities puts individuals and organizations at a major disadvantage

As a prelude to considering the characteristics of DSSs, we need to examine a couple of preliminaries: decision making and knowledge. Understanding what it means to make a decision provides a useful basis for exploring decision support possibilities. Understanding salient aspects of knowledge gives a starting point for appreciating ways in which computers can support the use of knowledge during decision making.

Decision Making. General agreement in the management literature exists that a decision is a choice. It may be a choice about a ‘‘course of action’’ , choice of a ‘‘strategy for action’’, or a choice leading to a certain desired objective’’. Thus, we can think of decision making as an activity culminating in the selection of one from among multiple alternative courses of action.

In general, the number of alternatives identified and considered in decision making could be very large. The work involved in becoming aware of alternatives often makes up a major share of a decision-making episode. It is concerned with such questions as “Where do alternatives come from?’’ ‘‘How many alternatives are enough?’’ ‘‘How can large numbers of alternatives be managed so none is forgotten or garbled?’’ A computer-based system (i.e, a DSS) can help a decision maker cope with such issues.

Ultimately, one alternative is selected. But, which one? This choice depends on a study of the alternatives to understand their various implications as well as on a clear appreciation of what is important to the decision maker. The work involved in selecting one alternative often makes up a major share of a decision-making episode.

It is concerned with such questions as: ‘‘To what extent should each alternative be studied?’’ ‘‘How reliable is our expectation about an alternative’s impacts?’’ ‘‘Are an alternative’s expected impacts compatible with the decision maker’s purposes?’’ ‘‘What basis should be used to compare alternatives with each other?’’ ‘‘What strategy will be followed in arriving at a choice?’’ Computer-based systems (i.e., DSSs) can be very beneficial in supporting the study of alternatives. Some systems even recommend the selection of a particular alternative and explain the rationale underlying that advice.

Complementing the classic view of decisions and decision making, there is the knowledge-based view that holds that a decision is knowledge that indicates the nature of an action commitment. When we regard a decision as a piece of knowledge, making a decision means we are making a new piece of knowledge that did not exist before, manufacturing new knowledge by transforming or assembling existing pieces of knowledge.

The manufacturing process may yield additional new knowledge as byproducts (e.g., knowledge derived as evidence to justify the decision, knowledge about alternatives that were not chosen, knowledge about improving the decision manufacturing process itself). Such byproducts can be useful later in making other decisions. A DSS is a computer-based system that aids the manufacturing process, just as machines aid in the manufacturing of material goods.

According to Mintzberg (6), there are four decisional roles: entrepreneur, disturbance handler, resource allocator, and negotiator. When playing the entrepreneur role, a decision maker searches for opportunities to advance in new directions aligned with his/her/its purpose. If such an opportunity is discovered, the decision maker initiates and devises controlled changes in an effort to seize the opportunity. As a disturbance handler, a decision maker initiates and devises corrective actions when facing an unexpected disturbance. As a resource allocator, a decision maker determines where efforts will be expended and how assets will be deployed.

This decision can be thought of as determining a strategy for structuring available resources. When playing the negotiator role, a decision maker bargains with others to try to reach a joint decision. Decision support systems are capable of supporting these four roles, although a particular DSS can be oriented more toward one role than the others.

A DSS can also vary to suit other particularities of contexts in which it is to be used. For instance, the context could be strategic decision making (concerned with deciding on purposes to fulfill, objectives to meet, changes in objectives, policies to adopt) decision making to ensure objectives are met and policies are observed, or operational decision making about performing specific tasks.

These contexts vary along such dimensions as time horizons for deciding, extent of precision and detailed knowledge needed, narrow to wide-ranging knowledge, rhythm to decision-making activities, and degree of creativity or qualitative judgment required.

As another example of decision context, consider the maturity of the situation in which a decision is being made. Some decisions are made in established situations, whereas others are made in emergent situations. Well-established situations imply considerable experience in previously having made similar kinds of decisions, with a relatively high level of knowledge existing about the current state of affairs and the history of previous decisions of a similar nature. In contrast, emergent situations are characterized not only by some surprising new knowledge, but also often by a scarcity of relevant knowledge as well, often with intense effort required to acquire needed knowledge. The type of support likely to be most useful for established contexts could be quite different than what is valuable in the case ofemergent settings.

Simon says that decisions comprise a continuum ranging from structured to unstructured. The structuredness of a decision is concerned with how routine and repetitive is the process that produced it. A highly structured decision is one that has been manufactured in an established context. Alternatives from which the choice is made are clear-cut, and each can be readily evaluated in light of purposes and goals. All knowledge required to make the decision is available in a form that makes it straightforward to use. Unstructured decisions tend to be produced in emergent contexts. Issues pertinent to producing a decision are not well understood.

Some issues may be entirely unknown to the decision maker; alternatives from which a choice will be made are vague or unknown, are difficult to compare and contrast, or cannot be easily evaluated. In other words, the knowledge required to produce a decision is unavailable, difficult to acquire, incomplete, suspect, or in a form that cannot be readily used by the decision maker. Semistructured decisions lie between the two extremes.

DSSs of varying kinds can be valuable aids in the manufacture of semistructured and unstructured decisions, as well as structured decisions. For the former, DSSs can be designed to facilitate the exploration of knowledge, help synthesize methods for reaching decisions, catalog and examine the results of brainstorming, provide multiple perspectives on issues, or stimulate a decision maker’s creative capabilities. For structured decisions, DSSs automatically carry out some portion of the process used to produce a decision.

Because decisions are not manufactured in a vacuum, an appreciation of decision contexts and types can help us understand what features would be useful to have in a DSS. The same can be said for an appreciation of decision makers and decision processes, which we now consider in turn.

Decision making can involve an individual participant or multiple participants. In the multiparticipant case, the power to decide may be vested in a single participant, with other participants having varying degrees of influence over what the decision will be and how efficiently it will be produced. They do so by specializing in assorted knowledge processing tasks assigned to them during the making of the decision.

These supporting participants function as extensions to the deciding participant’s own knowledge processing capabilities. At the other extreme of multiparticipant decision making, participants share equal authority over the decision being made, with little formal specialization in knowledge processing tasks. This is referred to as a group decision maker, whereas the other extreme is called an organization decision maker.

There are many variations between these extremes. Correspondingly, DSSs for these different kinds of multiparticipant decision makers can be expected to exhibit some different kinds of features. Moreover, a particular DSS may assist a specific participant, some subset of participants, or all participants involved in a multiparticipant decision.

Now, as for the process of decision making, Simon says there are three important phases, which he calls intelligence, design, and choice. Moreover, running through the phases in any decision-making process, a decision maker is concerned with recognizing and solving some problems in some sequence. A decision-making process is governed by the decision maker’s strategy for reaching a choice.

The intelligence phase is a period when the decision maker is alert for occasions to make decisions, preoccupied with collecting knowledge, and concerned with evaluating it in light of a guiding purpose. The design phase is a period wherein the decision maker formulates alternative courses of action, analyzes those alternatives to arrive at expectations about the likely outcomes of choosing each, and evaluates those expectations with respect to a purpose or objective.

During the design phase, the decision maker may find that additional knowledge is needed, triggering a return to the intelligence phase to satisfy that need before continuing with the design activity. Evaluations of the alternatives are carried forward into the choice phase of the decision process, where they are compared and one is chosen. This choice is made in the face of internal and external pressures related to the nature of the decision maker and the decision context.

It may happen that none of the alternatives are palatable, that several competing alternatives yield very positive evaluations, or that the state of the world has changed significantly since the alternatives were formulated and analyzed. So, the decision maker may return to one of the two earlier phases to collect more up- to-date knowledge, formulate new alternatives, reanalyze alternatives, reevaluate them, and so forth. Any phase is susceptible to computer-based support.

Recognizing and solving problems is the essence of activity within intelligence, design, and choice phases. For structured decisions, the path toward the objective of producing a decision is well charted. Problems to be surmounted are recognized easily, and the means for solving them are readily available. Unstructured decisions take us into uncharted territory. Problems that will be encountered along the way are not known in advance. Even when stumbled upon, they may be difficult to recognize and subsequently solve. Ingenuity and an exploratory attitude are vital for coping with these types of decisions.

Thus, a decision-making process can be thought of as a flow of problem-recognition and problem-solving exercises. In the case of a multiparticipant decision maker, this flow has many tributaries, made up of different participants working on various problems simultaneously, in parallel, or in some necessary sequence. Only if we solve its subproblems can we solve an overall decision problem. DSSs can help decision makers in recognizing and/or solving problems.

A decision-making process, and associated knowledge processing, are strongly colored by the strategy being used to choose an alternative. Well-known decision-making strategies include optimizing, satisficing, elimination-byaspects, incrementalism, mixed scanning, and the analytic hierarchy process. As a practical matter, each strategy has certain strengths and limitations. A DSS designed to support an optimizing strategy may be of little help when a satisficing strategy is being adopted and vice versa.

We close this brief overview of decision making by considering two key questions about decision support: Why does a decision maker need support? What is the nature of the needed support? Computer systems to support decision makers are not free. Not only is there the cost of purchasing or developing a DSS, costs are also associated with learning about, using, and maintaining a DSS. It is only reasonable that the benefits ofa DSS should be required to outweigh its costs. Although some DSS benefits can be difficult to measure in precise quantitative terms, all benefits are the result of a decision maker’s need for support in overcoming cognitive, economic, or time limits.

Cognitive limits refer to limits in the human mind’s ability to store and process knowledge. A person does not know everything all the time, and what is known cannot always be recalled in an instantaneous, error-free fashion. Because decision making is a knowledge-intensive activity, cognitive limits substantially restrict an individual’s problem-solving efficiency and effectiveness. They may even make it impossible for the individual to reach some decisions. If these limits are relaxed, decision-maker productivity should improve.

The main reason multiparticipant decision makers exist is because of this situation. Rather than having an individual find and solve all problems leading to a decision, additional participants serve as extensions to the deciding participant’s own knowledgehandling skills, allowing problems to be solved more reliably or rapidly. A DSS can function as a supporting participant in decision making, essentially extending a person’s cognitive capabilities.

To relax cognitive limits as much as possible, we could consider forming a very large team of participants. But this can be expensive not only in terms of paying and equipping more people, but also with respect to increased communication and coordination costs. At some point, the benefits of increased cognitive abilities are outweighed by the costs of more people. Decision support systems can soften the effects of economic limits when they are admitted as decision-making participants. If properly conceived and used, added DSSs increase the productivity of human participants and allow the organization decision maker to solve problems more efficiently and effectively.

A decision maker may be blessed with extraordinary cognitive abilities and vast economic resources but very little time. Time limits can put severe pressure on the decision maker, increasing the likelihood of errors and poor-quality decisions. There may not be sufficient time to consider relevant knowledge, to solve relevant problems, or to employ a desirable decision-making strategy. Because computers can process some kinds of knowledge much faster than humans, are not error-prone, work tirelessly, and are immune to stresses from looming deadlines, DSSs can help lessen the impacts of time limits.

To summarize, the support that a DSS offers normally includes at least one of the following:
- Alerts user to a decision-making opportunity or challenge
- Recognizes problems that need to be solved as part of the decision-making process
- Solves problems recognized by itself or by the user

- Facilitates or extends the user’s ability to process (e.g., acquire, transform, and explore) knowledge
- Offers advice, expectations, evaluations, facts, analyses, and designs to users
- Stimulates the user’s perception, imagination, or creative insight
- Coordinates/facilitates interactions within multiparticipant decision makers

Because knowledge forms the fabric of decision making, all the various kinds of support that a DSS can provide are essentially exercises in knowledge management. Thus, we now take a closer look at the matter of knowledge.