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Ch. 1
Supporting Business Decision-Making

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Alterís Taxonomy

In 1980, Steven Alter (pps. 73-93) proposed a taxonomy of DSS. The next few paragraphs summarize his taxonomy and discuss some of the key issues for each type of DSS. Alter's taxonomy is based on the degree to which DSS output can directly determine the decision. The taxonomy is related to a spectrum of generic operations that can be performed by Decision Support Systems. These generic operations extend along a single dimension, ranging from extremely data-oriented to extremely model-oriented. DSS may involve retrieving a single item of information, providing a mechanism for ad hoc data analysis, providing pre-specified aggregations of data in the form of reports or "screens". DSS may also include estimating the consequences of proposed decisions and proposing decisions.

Alter's idea was that a Decision Support System could be categorized in terms of the generic operations it performs, independent of type of problem, functional area or decision perspective. Alter conducted a field study of 56 DSS that he categorized into seven distinct types of DSS. His seven types include:

    • File drawer systems that provide access to data items. Examples include real-time equipment monitoring, inventory reorder and monitoring systems. Simple query and reporting tools that access OLTP fall into this category.
    • Data analysis systems that support the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators. Examples include budget analysis and variance monitoring, and analysis of investment opportunities. Most data warehouse applications would be categorized as data analysis systems.
    • Analysis information systems that provide access to a series of decision-oriented databases and small models. Examples include sales forecasting based on a marketing database, competitor analyses, product planning and analysis. OLAP systems fall into this category.
    • Accounting and financial models that calculate the consequences of possible actions. Examples include estimating profitability of a new product; analysis of operational plans using a goal-seeking capability, break-even analysis, and generating estimates of income statements and balance sheets. These types of models should be used with "What if?" or sensitivity analysis.
    • Representational models that estimate the consequences of actions on the basis of simulation models that include relationships that are causal as well as accounting definitions. Examples include a market response model, risk analysis models, and equipment and production simulations.
    • Optimization models that provide guidelines for action by generating an optimal solution consistent with a series of constraints. Examples include scheduling systems, resource allocation, and material usage optimization.
    • Suggestion models that perform the logical processing leading to a specific suggested decision for a fairly structured or well-understood task. Examples include insurance renewal rate calculation, an optimal bond-bidding model, a log cutting DSS, and credit scoring.

An understandable typology like Steven Alter's helps reduce the confusion for managers who are investigating and discussing Decision Support Systems. The taxonomy also helps users and developers communicate their experiences with DSS.


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