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Choosing the Right DSS System for Your Needs The most appropriate DSS depends upon organizational maturity, complexity and, to a certain extent, size. In small

Choosing the Right DSS System for Your Needs
The most appropriate DSS depends upon organizational maturity, complexity and, to a certain extent, size. In small
organizations, hybrid systems may suffice. If the organization is new to analytics, historical DSS systems would be a good
place to start, while those involved in activities such as trading and commodities may benefit more from a predictive decision
support system example.
Without a doubt, the greatest benefit lies with selecting a prescriptive analytics derived decision management system that
models the business and provides the ability to determine the most advantageous decision based on certain criteria, such as
revenue and profitability. While entailing a greater investment in resources and money, such a solution has a greater probability
of exceeding expectations and achieving a greater ROI. Additionally, it takes the guesswork out of decision
-
making, and
because the model replicates the business, this type of decision support system example is more likely to offer feasible and
rational solutions.
Common Day
-
to
-
Day Decision Support System Examples
Decision support systems operate at many levels, and there are many examples in common day
-
to
-
day use. For example, GPS
route planning determines the fastest and best route between two points by analysing and comparing multiple possible options.
Many GPS systems also include traffic avoidance capabilities that monitor traffic conditions in real time, allowing motorists to
avoid congestion. Farmers use crop
-
planning tools to determine the best time to plant, fertilize and reap. Medical diagnosis
software that allows medical personnel to diagnose illnesses is another example. Most systems share a common attribute in
that decisions are repetitive and based on known data. However, they aren
t infallible and may make incorrect or irrational
decisions, something many early GPS users discovered.
Decision Support System Examples That Use Historical Data
Historical data analysis, used in every facet of business and life, is well
-
developed and mature. Although such information is
not always directly actionable, it
s an important part of DSS because it reports past performance and highlights areas that need
attention. Some examples include:
Descriptive analytics: Metrics such as sales results, inventory turnover and revenue growth.
Diagnostic analytics: Diagnostic information that digs a bit deeper to reveal results and explains reasons for past
performance as measured by descriptive analytics.
Business intelligence
(
BI
)
: Although largely based on historical data, BI solutions allow users to develop and run queries
that are used to guide and support decision
-
making.
ERP dashboards: User
-
configurable dashboards that allow managers to monitor a variety of performance indicators. QUESTION 2
Electronic Data Interchange (EDI) involves the electronic exchange of business transaction documents over the
internet and other networks between supply chain trading partners. Data representing a variety of business
transaction documents are electronically exchanged between computers using standard document message
formats. Discuss the characteristics and benefits of utilising EDI in an organisation.

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