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Chapter 2 • Foundations and Technologies for Decision Making 93

in distributed computing environments, and what-if analyses. Many large-scale applica-
tions fall into this category. Notable examples include those used by Procter & Gamble
(Farasyn et al., 2008), HP (Olavson and Fry, 2008), and many others.
The focus of such systems is on using the model(s) to optimize one or more objec-
tives (e.g., profit). The most common end-user tool for DSS development is Microsoft
Excel. Excel includes dozens of statistical packages, a linear programming package
(Solver), and many financial and management science models. We will study these in
more detail in Chapter 9. These DSS typically can be grouped under the new label of
prescriptive analytics.

coMPounD Dss A compound, or hybrid, DSS includes two or more of the major cat-
egories described earlier. Often, an ES can benefit by utilizing some optimization, and
clearly a data-driven DSS can feed a large-scale optimization model. Sometimes docu-
ments are critical in understanding how to interpret the results of visualizing data from
a data-driven DSS.
An emerging example of a compound DSS is a product offered by WolframAlpha
(wolframalpha.com). It compiles knowledge from outside databases, models, algo-
rithms, documents, and so on to provide answers to specific questions. For example, it
can find and analyze current data for a stock and compare it with other stocks. It can
also tell you how many calories you will burn when performing a specific exercise or
the side effects of a particular medicine. Although it is in early stages as a collection of
knowledge components from many different areas, it is a good example of a compound
DSS in getting its knowledge from many diverse sources and attempting to synthesize it.

other Dss categories
Many other proposals have been made to classify DSS. Perhaps the first formal attempt
was by Alter (1980). Several other important categories of DSS include (1) institutional
and ad hoc DSS; (2) personal, group, and organizational support; (3) individual support
system versus GSS; and (4) custom-made systems versus ready-made systems. We discuss
some of these next.

institutional anD aD hoc Dss institutional Dss (see Donovan and Madnick, 1977)
deal with decisions of a recurring nature. A typical example is a portfolio management
system (PMS), which has been used by several large banks for supporting investment
decisions. An institutionalized DSS can be developed and refined as it evolves over a
number of years, because the DSS is used repeatedly to solve identical or similar prob-
lems. It is important to remember that an institutional DSS may not be used by everyone
in an organization; it is the recurring nature of the decision-making problem that deter-
mines whether a DSS is institutional versus ad hoc.
ad hoc Dss deal with specific problems that are usually neither anticipated nor recur-
ring. Ad hoc decisions often involve strategic planning issues and sometimes management
control problems. Justifying a DSS that will be used only once or twice is a major issue
in DSS development. Countless ad hoc DSS applications have evolved into institutional
DSS. Either the problem recurs and the system is reused or others in the organization have
similar needs that can be handled by the formerly ad hoc DSS.
custom-Made systems versus ready-Made systems

Many DSS are custom made for individual users and organizations. However, a com-
parable problem may exist in similar organizations. For example, hospitals, banks,
and universities share many similar problems. Similarly, certain nonroutine problems in
a functional area (e.g., finance, accounting) can repeat themselves in the same functional

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