Book excerpt

Using computerized decision support systems, and the history of DSS

In this excerpt from Decision Support Basics, readers will learn about the history of computerized decision support systems (DSS) – from the first generation of decision support technologies to modern DSS. Readers will also learn about a theory of computerized decision support systems and five attributes of contemporary DSS that set them apart from their predecessors.

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How decision support systems (DSS) can help business decision-making
 Using computerized decision support systems, and the history of DSS


What Is the History of Computerized Decision Support?

Supposedly, if we study some history, we are less likely to make the same mistakes again. Computerized decision support has had failures and successes. This brief review of the evolution of decision support technology touches primarily on DSS pioneers and their successes (see Figure 1.1). My online DSS history articles provide more details.

First-Generation Decision Support
Some researchers trace the origins of computerized decision support systems to 1951 and the Lyons Tea Shops business use of the LEO I (Lyons Electronic Office I) digital computer. LEO handled the company’s accounts and logistics. Software factored in the weather forecast to help determine the goods carried by “fresh produce” delivery vans to Lyons’s UK shops.

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A few years later, work started on the Semi-Automatic Ground Environment (SAGE), a control system for tracking aircraft used by NORAD from the late 1950s to the early 1980s. The name SAGE, a wise mentor, indicated the decision support nature of the system. SAGE was a real-time control system, a real-time communication system, and a real-time management information system.

The pioneering work of George Dantzig, Douglas Engelbart, and Jay Forrester established the feasibility of building computerized decision support systems. In 1952, Dantzig became a research mathematician at the Rand Corporation, where he implemented linear programming on its experimental computers. In the mid-1960s, Engelbart and colleagues developed the first hypermedia groupware system, called NLS (oNLine System). NLS had on-screen video teleconferencing and was a forerunner to group decision support systems. Forrester was involved in building SAGE. In addition, Forrester started the System Dynamics Group at the Massachusetts Institute of Technology Sloan School.

Prior to about 1965, it was very expensive to build large-scale information systems. From 1965 onward, the IBM System 360 and other more powerful mainframe and minicomputer systems made it more practical and cost-effective to develop management information systems (MIS) in large companies. MIS focused on providing managers with structured, periodic reports derived from accounting and transaction systems.

Moving to the Next Generation
In the late 1960s, a new type of information system became practical: model-oriented DSS or management decision systems. In 1971, Michael S. Scott Morton published his Harvard Business School doctoral research involving a computerized management decision system. He had studied how computers and analytical models could help managers make a key decision. Scott Morton conducted an experiment where marketing and production managers used a management decision system to coordinate production planning for laundry equipment. The decision system ran on a 21-inch cathode ray tube monitor with a light pen connected using a 2,400-bits-per-second modem to a pair of Univac 494 computer systems.

In 1971, Gorry and Scott Morton argued that MIS primarily focused on structured decisions and suggested that the information systems for semi-structured and unstructured decisions should be termed decision support systems.

In the late 1970s, researchers were discussing both practice and theory issues related to decision support systems, and companies were implementing a variety of systems. In 1979, John Rockart published an article in the Harvard Business Review that led to the development of executive information systems (EIS). In 1980, Steven Alter published a framework for categorizing decision support systems based on studying 58 DSS. He identified both data-oriented and model-oriented DSS.

Ralph Sprague and Eric Carlson’s book, Building Effective Decision Support Systems, explained in detail the Sprague DSS framework of a database, model base, and dialog generator. In addition, they provided a practical, understandable overview of how organizations could and should build DSS. By 1982, researchers considered decision support systems a new class of information systems.

Financial planning systems became especially popular decision support tools. The idea was to create a “language” that would “allow executives to build models without intermediaries.”

Thirty years after Lyons Tea used a computerized system to support operations decision making, managers and researchers recognized that DSS could support decision makers at any level in an organization. DSS could support operations, financial management, management control, and strategic decision making. The scope, purpose, and targeted users for a computerized DSS were expanding.

Expanding Decision Support Technologies
Beginning in approximately 1982, academic researchers developed software to support group decision making.  In 1985, Procter & Gamble built a DSS that linked sales information and retail scanner data. BI described a set of concepts and methods to improve business decision making by using fact-based support systems. Some people used BI interchangeably with briefing books, report and query tools, and EIS. Data warehousing and online analytical processing (OLAP) defined a broader category of data-driven DSS.

In the early 1990s, Bill Inmon and Ralph Kimball actively promoted using relational database technologies to build DSS. Kimball was known as “the doctor of DSS,” and Inmon became the “father of the data warehouse.” Inmon defined a decision support system as “data used in a free form fashion to support managerial decisions.” The DSS environment contained only archival, time variant data.

A major technology shift had occurred from mainframe and timesharing DSS to client or server-based DSS. Vendors introduced desktop OLAP tools during this period. DBMS vendors “recognized that decision support was different from OLTP and started implementing real OLAP capabilities into their databases.” By 1995, large-scale data warehousing, a convergence of OLAP, EIS and BI, and the possibilities of the World Wide Web began to stimulate innovation and created a renewed interest in decision support systems.


What Is the Theory of Computerized Decision Support Systems?


Past practice and experience often guide computerized decision support development more than theory and general principles. Some developers say each situation is different so no theory is possible. Others argue that we have conducted insufficient research to develop theories. For these reasons, the theory of decision support and DSS has not been addressed extensively in the literature.

The following set of six propositions from the writings of the late Nobel Laureate Economist Herbert Simon form an initial theory of decision support. From Simon’s classic book, Administrative Behavior, we draw three propositions.

Proposition 1: If information stored in computers is accessible when needed for making a decision, it can increase human rationality.

Proposition 2: Specialization of decision-making functions is largely dependent upon developing adequate channels of communication to and from decision centers.

Proposition 3: When a particular item of knowledge is needed repeatedly in decision making, an organization can anticipate this need and, by providing the individual with this knowledge prior to decision, can extend his or her area of rationality. Providing this knowledge is particularly important when there are time limits on decisions.

From Simon’s article 18 on “Applying Information Technology to Organization Design,” we identify three additional propositions:

Proposition 4: In the post-industrial society, the central problem is not how to organize to produce efficiently but how to organize to make decisions that is, to process information. Improving efficiency will always remain an important consideration.

Proposition 5: From the information processing point of view, division of labor means factoring the total system of decisions that need to be made into relatively independent subsystems, each one of which can be designed with only minimal concern for its interactions with the others.

Proposition 6: The key to the successful design of information systems lies in matching the technology to the limits of the attention of users. In general, an additional component, person, or machine for an information-processing system will improve the system’s performance when the following three conditions are true:

1. The component’s output is small in comparison with its input so that it conserves attention instead of making additional demands on attention.

2. The component incorporates effective indexes of both passive and active kinds. Active indexes automatically select and filter information.

3. The component incorporates analytic and synthetic models that are capable of solving problems, evaluating solutions, and making decisions.

In summary, computerized decision support is potentially desirable and useful when there is a high likelihood of providing relevant, high quality information to decision makers when they need it and want it.


What Is Different About Modern Decision Support Systems? 

The modern era in decision support systems started in about 1995 with the specification of HTML 2.0, the expansion of the World Wide Web in companies, and the introduction of handheld computing. Today, the Web 2.0 technologies, mobile-integrated communication and computing devices, and improved software development tools have revolutionized DSS user interfaces. Additionally, the decision support data store back-end is now capable of rapidly processing very large data sets.

Modern DSS are more complex and more diverse in functionality than DSS built prior to the widespread use of the World Wide Web. Today, we are seeing more decision automation with business rules and more knowledge-driven decision support systems. Current DSS are changing the mix of decision-making skills needed in organizations. Building better DSS may provide one of the “keys” to competing in a global business environment.

The following attributes are increasingly common in new and updated decision support systems. Some attributes are more closely associated with one category of DSS, but sophisticated DSS often have multiple subsystems. Attributes of contemporary DSS include the following:

1. Multiple, remote users can collaborate in real-time using rich media.

2. Users can access DSS applications anywhere and anytime.

3. Users have fast access to historical data stored in very large data sets.

4. Users can view data and results visually with excellent graphs and charts.

5. Users can receive real-time data when needed.



Decision support systems differ in purpose, targeted users, and technologies. With current technologies, we can support a wide range of decision-making tasks. Today’s complex decision-making environment creates a need for more, and better, computerized decision support.

A brief review of DSS history provides a context for understanding these systems. First-generation DSS were on mainframe computers, but the SAGE system provided real-time decision support. New decision support technologies in the 1980s broadened the possibilities for computerized decision support.

Nobel Laureate Herbert Simon’s ideas provide a theoretical rationale for building computerized decision support systems. Modern decision support systems exploit new technologies and have extensive capabilities. We can build effective decision support systems.

Table of Contents

How decision support systems (DSS) can help business decision-making
 Using computerized decision support systems, and the history of DSS

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This was first published in October 2010

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