Page 43 - DCAP208_Management Support Systems

Management Support Systems

Notes 12. The ....................... is the part of the system you see through it when enter information,
commands, and models.
13. The ....................... component provides information about the relationship among data
that is too complex for a database to represent.
14. The function of managing models in model management is similar to that of a .......................
15. Businesses use models to represent ....................... and their relationships.

Case Study An Intelligent Decision Support System

nformation technology applications that support decision-making processes and
problem-solving activities have proliferated and evolved over the past few decades.
IIn the 1970s, these applications were simple and based on spreadsheet software. During
the 1980s, decision support systems incorporated optimization models, which originated
in the operations research and management science communities. In the 1990s, these
systems were further enhanced with components from artificial intelligence and statistics.
This evolution led to many different types of decision support systems with somewhat
confusing names, including management information systems, intelligent information
systems, expert systems, management support systems, and knowledge-based systems.
Because businesses realized that data was a precious asset, they often based these
“intelligent” systems on data warehousing and online analytical processing technologies.
They gathered and stored a lot of data, assuming valuable assets were implicitly coded in
it. Raw data, however, is rarely beneficial. Its value depends on a user’s ability to extract
knowledge that is useful for decision support. Thousands of “business intelligence”
companies thus emerged to provide such services. After analyzing a corporation’s
operational data, for example, these companies might return intelligence (in the form of
tables, graphs, charts, and so on) stating that, say, 57 percent of the corporation’s customers
are between 40 and 50, or product Q sells much better in Florida than in Georgia.

Many businesses have realized, however, that the return on investment for pure “business
intelligence” is much smaller than initially thought. The “discovery” that 57 percent of
your customers are between 40 and 50 doesn’t directly lead to decisions that increase
profit or market share. Moreover, we live in a dynamic environment where everything is
in flux.
Interest rates change, new fraud patterns emerge, weather conditions vary, the stock
markets rise and fall, new regulations and policies surface, and so on. These economic and
environmental changes render some data obsolete and make other data—which might
have been useless just six weeks ago—suddenly meaningful. We developed a software
system to address these complexities and implemented it on a real distribution problem
for a large car manufacturer. The system detects data trends in a dynamic environment,
incorporates optimization modules to recommend a near-optimum decision, and includes
self-learning modules to improve future recommendations. As figure 1 shows, such a
system lets enterprises monitor business trends, evolve and adapt quickly as situations
change, and make intelligent decisions based on uncertain and incomplete information.

This diagram shows the flow from data acquisition to recommended action, including an
adaptive feedback loop.
Contd....

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