Decision Support System Roots, Characteristics, and Benefits

Rooted in an understanding of decision making, appreciating the purposes of DSSs serves as a starting point for identifying possible characteristics and benefits that we might expect a DSS to exhibit.

These purposes include:
- Increase a decision maker’s efficiency and/or effectiveness
- Help a decision maker successfully deal with the decisional context
- Aid one or more of the three decision-making phases
- Help the flow of problem-solving episodes proceed more smoothly or rapidly
- Relax cognitive, economic, and/or temporal constraints on a decision maker
- Help manage DPR knowledge that is important for reaching decisions

Decision support systems are deeply rooted in the evolution of business computing systems (aka information systems). Another way to appreciate characteristics and benefits of DSSs is to compare/contrast them with traits of their two predecessors in this evolution: data processing systems (DPS) and management information systems (MIS). All three share the traits of being concerned with record keeping; however, they differ in various ways, because each serves a different purpose in managing knowledge resources.

The main purpose of DPS was and is to automate the handling of large numbers of transactions. For example, a bank must deal with large volumes of deposit and withdrawal transactions every day, properly track each transaction’s effect on one or more accounts, and maintain a history of all transactions to give a basis for auditing its operations. At the heart of a DPS lies a body of descriptive knowledge—computerized records of what is known as a result of transactions having happened.

A data processing system has two major abilities related to the stored data: record keeping and transaction generation. The former keeps the records up-to-date in light of incoming transactions and can cause creation of new records, modification of existing records, deletion of obsolete records, or alteration of relationships among records. The second DPS ability is production of outgoing transactions based on stored descriptive knowledge, and transmitted to such targets as customers, suppliers, employees, or governmental regulators.

Unlike DPS, the central purpose of MIS was and is to provide periodic reports that recap certain predetermined aspects of past operations. They give regular snapshots of what has been happening. For instance, MIS might provide manufacturing managers with daily reports on parts usage and inventory levels, weekly reports on shipments received and parts ordered, a monthly report of production expenditures, and an annual report on individual workers’ levels of productivity.

Whereas DPS concern is with transforming transactions into records and generating transactions from records, MIS concern with record keeping focuses on using this stored descriptive knowledge as a base for generating recurring standard reports. Of course, an MIS also has facilities for creating and updating the collection of records that it keeps. Thus, an MIS can be regarded as extending the DPS idea to emphasize production of standard reports rather than producing voluminous transactions for customers, suppliers, employees, or regulators.

Information contained in standard MIS reports certainly can be factored into their users’ decision-making activities. When this is the case, MIS can be fairly regarded as a kind of DSS. However, the nature of such support is very limited in light of our understanding of decision making. Reports generated by MIS are defined before the system is created. However, the situation surrounding a decision maker can be very dynamic.

Except for the most structured kinds of decisions, information needs can arise unexpectedly and change more rapidly than MIS can be built or revised. Even when some needed information exists in a stack of reports accumulated from MIS, it may be buried within other information held by a report, scattered across several reports, and presented in a fashion not suitable for a user. Moreover, relevant information existing in MIS reports may not only be incomplete, difficult to dig out, unfocused, or difficult to grasp, it may also be in need of additional processing.

For instance, a series of sales reports may list daily sales levels for various products, when a user actually needs projections of future sales based on data in these reports. Decision making proceeds more efficiently and effectively when a user can easily get complete, fully processed, focused descriptive knowledge (or even procedural and reasoning knowledge) presented in the desired way.

Standard reports generated by MIS are typically issued at set time intervals. But decisions that are not fully structured tend to be required at irregular, unanticipated times. The knowledge needed for manufacturing decisions must be available on an ad hoc, spur-of-the-moment, asneeded basis.

Another limit on MIS ability to support decisions stems from their exclusive focus on managing descriptive knowledge. Decision makers frequently need procedural and/or reasoning knowledge as well. While an MIS deals with domain knowledge, decision making can often benefit from relational and self-knowledge possessed by its participants.

Decision support capabilities can be built on top of DPS and MIS functions. For instance, so-called digital dash-boards are a good example. A digital dashboard integrates knowledge from multiple sources (e.g., external feeds and departmental DPSs and MISs) and can present various measures of key performance indicators (e.g., sales figures, operations status, balanced scorecards, and competitor actions) as an aid to executives in identifying and formulating problems in the course of decision making.

Executives can face decisions, particularly more strategic decisions, that involve multiple inter-related issues involving marketing, strategy, competition, cash flow, financing, outsourcing, human resources, and so forth. In such circumstances, it is important for the knowledge system contents to be sufficiently wide ranging to help address cross-functional decisions.

DSS Characteristics. Ideally, a decision maker should have immediate, focused, clear access to whatever knowledge is needed on the spur-of-the-moment. Pursuit of this ideal separates decision support systems from their DPS and MIS ancestors. It also suggests characteristics we might expect to observe in a DSS:

- A DSS includes a body of knowledge that describes some aspects of the decision domain, that specifies how to accomplish various tasks, and/or that indicates what conclusions are valid in various circumstances.

- A DSS may also possess DPR knowledge of other decision-making participants and itself as well.
- A DSS has an ability to acquire, assimilate, and alter its descriptive, procedural, and/or reasoning knowledge.

- A DSS has an ability to present knowledge on an ad hoc basis in various customized ways as well as in standard reports.
- A DSS has an ability to select any desired subset of stored knowledge for either presentation or deriving new knowledge in the course of problem recognition and/or problem solving.

- DSS can interact directly with a participant in a decision maker in such a way that there is flexibility in choosing and sequencing knowledge manipulation activities.

There are, of course, variations among DSSs with respect to each of these characteristics. For instance, one DSS may possess descriptive and procedural knowledge, another holds only descriptive and reasoning knowledge, and another DSS may store only descriptive knowledge. As another example, there can be wide variations in the nature of users’ interactions with DSSs (push versus pull interactions). Regardless of such variations, these characteristics combine to amplify a decision maker’s knowledge management capabilities.

The notion of DSSs arose in the early 1970s. Within a decade, each of the characteristics cited had been identified as an important DSS trait. In that period, various DSSs were proposed or implemented for specific decision-making applications such as those for corporate planning, water-quality planning banking, and so forth.

By the late 1970s, new technological developments were emerging that would prove to give tremendous impetus to the DSS field. These developments included the microcomputer, electronic spreadsheets, management science packages, and ad hoc query interfaces. Technological advances impacting the DSS field have continued, including progress in artificial intelligence, collaborative technologies, and the Internet.

It is also notable that DSS characteristics are increasingly appearing in software systems not traditionally thought of as providing decision support, such as enterprise systems. They also commonly appear in websites, supporting decisions of both users and providers of those sites.