The Role of Computers in Management Decision Making


The day when a computer makes the correct business decision time and time again will probably never come. For uncertainty shadows every business decision. And no amount of raw computational power can eliminate it, nor can intuition and seat-of-the pants experience be coded into a computer program. Still, computers can and do aid the decision maker in a number of concrete ways.


  • A Decision Support System (DSS) collects data from a host of internal and external sources, organizes and condenses these raw numbers into key indicators and builds "what-if" scenarios around them. Decision makers then evaluate the alternate solutions the DSS proposes . Real-world decisions are often complex and very few are based on "perfect' knowledge. A DSS can only ask probing questions, supply additional data upon request, help flow-chart complex processes and plug different variables into simulation models. The user makes the actual decision.


  • People have to feel comfortable using a DSS otherwise it will remain idle. An intuitive interface that relies on graphic representations of high-level objects and icon-links to analytic tools, automatic entry and formatting of data downloads, and an extensive help function make a DSS user friendly. What is inside the black box matters too. A DSS must support a host of business-related analytic functions, including: Internal Rate of Return calculations, cash flow tracking, resource-allocation prioritization, product-position mapping, consumer research, multi-stage and multi-variable forecasting, econometric modeling, and concept ranking.


  • The idea of designing formal computer programs to aid business decision making dates back to the 1980s. Early models tailor made to specific client needs caught the imagination of designers and theorists. By the end of the decade, their ideas and experiences coalesced into the Decision Support System. Commercially available, generic DSS software appeared in the 1990s. A variant called the Expert System soon followed. Today the statistical and financial functions embedded in Excel and other popular worksheet programs allow users to create their own rudimentary DSS.


  • A DSS built around a set of predetermined financial and statistical equations used to simulate real-world events is model driven. One that facilitates collaboration among decision makers is communications driven. A data-driven DSS, meanwhile, collects and crunches numbers in ways stipulated by the decision maker. But one that gathers and stores all the database entries, spreadsheets and written analyses that decision makers deem important is document driven. A knowledge-driven DSS or Expert System, finally, revolves around a set of structured rules the user applies.


  • An effective DSS compensates for the user's limitations, be they conceptual blind spots, for example, unfamiliarity with financial analysis or knowledge of simulation modeling. It makes use of all available resources to present the most complete picture of the problem possible. And it poses questions and more questions designed to encourage "if, then" thinking to narrow down options. All this requires very robust, very adaptable, interactive software capable of simultaneously dealing with staggering amounts of detail and high-level abstract concepts.

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