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Chapter 2 • Foundations and Technologies for Decision Making 99

(i.e., text in a human language) so that the users can easily express themselves in a mean-
ingful way. Because of the fuzzy nature of human language, it is fairly difficult to develop
software to interpret it. However, these packages increase in accuracy every year, and they
will ultimately lead to accurate input, output, and language translators.
Cell phone inputs through SMS are becoming more common for at least some con-
sumer DSS-type applications. For example, one can send an SMS request for search on
any topic to GOOGL (46645). It is most useful in locating nearby businesses, addresses,
or phone numbers, but it can also be used for many other decision support tasks. For
example, users can find definitions of words by entering the word “define” followed by a
word, such as “define extenuate.” Some of the other capabilities include:

• Translations: “Translate thanks in Spanish.”
• Price lookups: “Price 32GB iPhone.”
• Calculator: Although you would probably just want to use your phone’s built-in
calculator function, you can send a math expression as an SMS for an answer.
• Currency conversions: “10 usd in euros.”
• Sports scores and game times: Just enter the name of a team (“NYC Giants”), and Google
SMS will send the most recent game’s score and the date and time of the next match.
This type of SMS-based search capability is also available for other search engines, includ-
ing Yahoo! and Microsoft’s new search engine Bing.
With the emergence of smartphones such as Apple’s iPhone and Android smart-
phones from many vendors, many companies are developing applications (commonly
called apps) to provide purchasing-decision support. For example, Amazon.com’s app
allows a user to take a picture of any item in a store (or wherever) and send it to Amazon.
com. Amazon.com’s graphics-understanding algorithm tries to match the image to a real
product in its databases and sends the user a page similar to Amazon.com’s product
info pages, allowing users to perform price comparisons in real time. Thousands of
other apps have been developed that provide consumers support for decision making
on finding and selecting stores/restaurants/service providers on the basis of location,
recommendations from others, and especially from your own social circles.
Voice input for these devices and PCs is common and fairly accurate (but not per-
fect). When voice input with accompanying speech-recognition software (and readily
available text-to-speech software) is used, verbal instructions with accompanied actions
and outputs can be invoked. These are readily available for DSS and are incorporated into
the portable devices described earlier. An example of voice inputs that can be used for
a general-purpose DSS is Apple’s Siri application and Google’s Google Now service. For
example, a user can give her zip code and say “pizza delivery.” These devices provide the
search results and can even place a call to a business.
Recent efforts in business process management (BPM) have led to inputs directly
from physical devices for analysis via DSS. For example, radio-frequency identification
(RFID) chips can record data from sensors in railcars or in-process products in a factory.
Data from these sensors (e.g., recording an item’s status) can be downloaded at key loca-
tions and immediately transmitted to a database or data warehouse, where they can be
analyzed and decisions can be made concerning the status of the items being monitored.
Walmart and Best Buy are developing this technology in their SCM, and such sensor
networks are also being used effectively by other firms.

the knowledge-based Management subsystem
The knowledge-based management subsystem can support any of the other subsystems or
act as an independent component. It provides intelligence to augment the decision mak-
er’s own. It can be interconnected with the organization’s knowledge repository (part of

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