Transcript Supporting Business Decision-Making

Supporting Business
Decision-Making
Good Information is Essential
for Fact-Based DecisionMaking
The Importance of Knowledge
For centuries managers have used the
knowledge available to them to make
decisions
 The amount of knowledge used to make
decisions has increased exponentially
 The Importance of Decision Making
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 Decisions
today determine the landscape of
tomorrow's world
Decision Making
The common thread that runs through all
managerial functions
 Decision = a choice of one course of
action from a number of alternatives
leading to a certain desired objective
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Classifying Decisions
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Functional area
 Finance
decisions
 Marketing decisions
 Production decisions
 Personnel decisions, etc.
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Managerial Function
 Planning
decisions
 Organizing decisions
 Control decisions, etc.
Classifying Decisions
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Management Level
 Strategic
decisions
 Tactical decisions
 Operational decisions
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Structure of decision
 Structured/Programmed
decisions
 Semi-structured decisions
 Unstructured decisions
Decision Support System Definition
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A decision Support System is an interactive computer-based system
or subsystem that helps people use computer communications,
data, documents, knowledge and models to identify and solve
problems, complete decision process tasks, and make decision
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“DSS comprise a class of information system that draws on
transaction processing systems and interacts with the other parts of
the overall information system to support the decision-making
activities of managers and other knowledge workers in
organizations” (Sprague and Carlson, 1982, p. 9).
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DSS are ancillary or auxiliary systems; they are not intended to
replace skilled decision-makers
Reference - Power (2008)
DSS Assumptions
Is good information and analysis essential
for fact-based decision-making?
 Build DSS when good information is likely
to improve decision-making
 Build DSS when managers need and want
computerized decision support
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Reference - Power (2008)
MIS and DSS Brief History
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Late 1960s, MIS focused on providing structured, periodic reports
Late 1960s, first DSS built using interactive computer systems,
Scott-Morton
1975-1980 DSS using financial models with “What if?” analysis
1975 Steve Alter MIT dissertation
1979-1982 Theoretical foundations
Mid-1980s Executive Information Systems and GDSS
Early 1990s shift to client/server DSS, Business Intelligence, Bill
Inmon and Ralph Kimball
1995 Data warehousing, data mining and the world-wide web
1998 Enterprise performance management and balanced scorecard
2000 Application service providers (ASPs) and portals
Reference - Power (2008)
DSS History - Specifics
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1951 Lyons Tea Shops used LEO 1 digital computer to factor in weather forecasts to
determine what “fresh produce” delivery vans would carry to Lyon’s UK shops
Later SAGE a control system for tracking aircraft used by NORAD from the 1950s to
the early 1980s (real time control, communications)
Mid-1960s NLS first hypermedia groupware system was the forerunner to GDSS
1965 more cost effective due to the IBM System 360 and other more powerful
mainframes and minicomputer systems
1970s companies were implementing a variety of DSS
1982 DSS considered a new class of IS
1980s financial planning systems became popular “What-if” analysis
Mid-1980s DSS were supporting managers in operations, financial management,
management control and strategic decision making (scope, purpose and targeted
user base was expanding)
1985 P&G built a DSS that linked sales information and retail scanner data
Reference - Power (2008)
DSS Conceptual Perspective
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DSS are both off-the-shelf, packaged application
and custom designed systems.
Alter (1980)
 Designed
specifically to facilitate a decision process
 Should support rather than automate decision making
 Should be able to respond quickly to changing needs
of decision makers
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Business intelligence, knowledge management
Reference - Power (2008)
Characteristics of DSS
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Body of knowledge
Record keeping
Provide structure for a particular decision
Decision maker interacts directly with DSS
Facilitation
Ancillary. Not intended to replace decision makers
Repeated used
Task-oriented
Identifiable
Decision impact. Improve accuracy, timeliness, quality and overall
effectiveness of a specific decision or a set of related decision
Reference - Power (2008)
Characteristics of Decision Support
Information
Right Information – accurate, relevant and
complete
 Right Time – current, timely information
 Right Formation – easy to understand and
manipulate
 Right Cost – Cost/Benefit Trade-off
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Reference - Power (2008)
Is a DSS an MIS?
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MIS describe a broad, general category of
information systems or a functional reporting
system.
MIS is used to identify an academic major
Data-Driven DSS meet management reporting
needs
Decision Support Systems is a broad category of
interactive, analytical management information
systems
Reference - Power (2008)
Transaction Processing
What is a transaction? A work task
recorded by a data capture system. i.e.,
Purchase, order, payment
 Record current information but does not
maintain a database of historical
information
 Emphasize data integrity and consistency
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Reference - Power (2008)
DSS vs. Transaction Processing
Systems (TPS)
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TPS is designed to expedite and automate
transaction processing, record keeping, and
business reporting
TPS is related to DSS because TPS provides
data for reporting systems and data warehouses
DSS are designed to aid in decision-making
tasks and/or decision implementation
Reference - Power (2008)
DSS Applications
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Major airlines use DSS for many tasks including pricing and route
selection
DSS aid in corporate planning and forecasting
Specialists use DSS that focus on financial and simulation models
Frito-Lay has a DSS that aids in pricing, advertising, and promotion
Monsanto, FedEx and most transportation companies use DSS for
scheduling trucks, airplanes and ship
Wal-Mart has large data warehouses and data mining systems
There are many DSS on the Internet that help track and manage
stock portfolios, choose stocks, plan trips, and suggest gifts
Alter’s Categories of DSS
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Data-Driven
 File Drawer Systems
 Data Analysis Systems
 Analysis Information Systems
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Model-Driven
 Accounting and Financial
 Representational Models
 Optimization Models
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Knowledge-Driven
 Suggestion
Reference - Power (2008)
Models
Models
Alter’s Categories of DSS
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Data-Driven
 File
Drawer Systems
 Data Analysis Systems
 Analysis Information Systems
Reference - Power (2008)
Alter’s Categories of DSS
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Model-Driven
 Accounting
and Financial Models
 Representational Models
 Optimization Models
Reference - Power (2008)
Alter’s Categories of DSS
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Knowledge-Driven
 Suggestion
Reference - Power (2008)
Models
Framework
Primary framework dimension is the
dominant component or driver of the
decision support system (Power, 2002)
 Secondary dimensions are
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 The
intended or targeted users,
 The specific purpose of the system
 The primary deployment or enabling
technology
Reference - Power (2008)
Identify the system component that
provides primary functionality 
dominant component
Communication technologies
 Data and data management
 Documents and document management
 Knowledge base and processing
 Models and model processing
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Reference - Power (2008)
DSS Framework
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Communications-driven DSS
 Interactive
computer-based systems intended
to facilitate the solution of problems by
decision-makers working as a group
 Group DSS may be communications-driven or
model-driven
Reference - Power (2008)
DSS Framework
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Data-driven
 Includes
File Drawer/Management Reporting, Data
Warehousing and Analysis Systems, Executive
information Systems (EIS), and Geographic
Information Systems external data
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Emphasize access to and manipulation of large databases
and especially a time-series of internal company data and
sometimes external data
Document-driven DSS
 Retrieve
and manage unstructured documents and
web pages
Reference - Power (2008)
DSS Framework
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Knowledge-driven
 Built
using AI tools, data mining tools and
management expert systems
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Model-driven
 Include
systems that use accounting and financial
models, representative models, and optimization
models
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Emphasize access to and manipulation of a model, Whit If?
analysis
Reference - Power (2008)
DSS Framework
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Intended Users, e.g. Inter-Organizational DSS
 Designed
for customers and suppliers
 Data, model, document, knowledge, or
communications-driven
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Purpose, e.g. Function and Industry-Specific
DSS
 A DSS
that is designed specifically for a narrow task
 Specific rather than General purpose
 Vertical Market/Industry-Specific
Reference - Power (2008)
Describing a Specific DSS
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A web-based, model-driven DSS for truck
routing used by a dispatcher
A handheld PC-based, knowledge-driven DSS
for accident scene triage used by an EMT
A web-enabled, data-driven DSS for real-time
performance monitoring used by a factory
manager
A PC-based, model-driven DSS for planning
supply chain activities used by logistics staff
Reference - Power (2008)
Enabling Technology
USE the Web to deliver and category of
DSS = Web-based DSS
 Web-based, Communications-driven DSS
 Web-based, Data-driven DSS
 Web-based, Document-driven DSS
 Web-based, Knowledge-driven DSS
 Web-based, Model-driven DSS
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Reference - Power (2008)
Building DSS - components
External Data
• Dow Jones
• Reuters
Internal Data
• Personnel
• Production
• Finance
• Marketing
Database Component
• Knowledge
• Data
• Documents
Model Component
• Interface Engine
• Models
Communications Component
• DSS Architecture
• Network
• Web server
• Client/Server
• Mainframe
User Interface Component
• Dialog
• Maps
• Menus, Icons
• Representations
• Charts, graphs
• Web Browser
Users
Reference - Power (2008)
Building DSS – User Interface
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User Interface
 Most
Important Component
 Tools needed
DSS Generator
 Query & Reporting Tools
 Front-End Development Packages
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Reference - Power (2008)
Building DSS – Database
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Database
 Collection
of current and historical data from a
number of sources
 Large databases are called data warehouses
or data marts
 Size of data warehouses are discussed in
terms of multiple Terabytes (TB)
Reference - Power (2008)
Building DSS – Models
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Mathematical and Analytical Tools
 Used
and manipulated by managers
 Each Model-driven DSS has a specific
purpose
 Values of key variables and parameters are
frequently changed – “What IF?” analysis
Reference - Power (2008)
Building DSS – Architecture
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DSS Architecture and Networking
 How
hardware is organized
 How software and data are distributed and
organized
 How components of the system are integrated
and connected
 Communications component
Reference - Power (2008)
Challenges of DSS
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Rapid technology change
Managers as users and customers
Major issues
 What
to computerize?
 What data? Source?
 What processing and presentation?
 Are current DSS results decision-impelling?
 What technology for a new DSS?
Reference - Power (2008)
Gaining Competitive Advantage
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DSS can create a Competitive Advantage
if the following 3 criteria are met
 Must
be a major or significant strength or capability of
the organization
 DSS must be unique and proprietary to the
organization
 DSS must be sustainable for approximately 3 years
How can DSS provide a
competitive advantage?
Internet technologies have opened doors
for innovative Web-based DSS
 Inter-organizational DSS can improve
linkages with customers and suppliers
 Increasing efficiency and eliminate staff
and activities, cost advantage
 New products and services, differentiation
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How can DSS provide a
competitive advantage?
Communications-Driven DSS can remove
time and location barriers
 Increase focus on specific customer
segments
 Better fact-based decision-making
 Decrease decision cycle time
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Strategic DSS Examples
Frito-Lay
 L.L. Bean
 Lockheed - Georgia
 Mrs. Fields Cookies
 Wal-Mart
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Classic
examples!!
A company needs to continually invest in a
Strategic DSS to maintain any advantage.
Reference - Power (2008)
Frito-Lay
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Route Sales people were all given a handheld computer
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sales people to have decisionmaking role
 Allows Frito-Lay to track products
 The data is put into a Data-Driven DSS
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Automated a cumbersome process and improved
the quality of data
Reference - Power (2008)
L.L. Bean
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Consultants hired to design a system that would provide
better allocation of resources in telemarketing
Economic Optimization Model System (EOM)
 This Model-Driven DSS examined variables such as
the number of telephone lines to carry incoming
traffic, number of agents, and the queue capacity
 System generates specific resource amounts the
company should deploy to be most economically
advantageous
Reference - Power (2008)
Mrs. Fields Cookies
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Developed MIS in early 1980’s to provide
uniformity in store management; also
supporting rapid expansion
 Designed
to serve two purposes
 Control and better management decisionmaking
 Enabled each store to be run as Debbie Field
ran the original store
Reference - Power (2008)
Mrs. Fields Cookies
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Knowledge-Driven DSS developed that
automated routine activities and responded to
exceptions by prompting the store manager for
input
 Tracked financial performance of each store,
provided comprehensive scheduling of
operations, including market support, hourly
sales goals, and assisted with candidate
interview selection
Reference - Power (2008)
Wal-Mart
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Creates a competitive advantage that other
retailers have tried to mimic but have not
duplicated
 Result of Retail Link and FAR
 Less inventory in stores, more inventory of the right products
at the right time and place, and improved revenues for both
supplier and retailer
 Collaborative Forecasting and Replenishment Initiative
(CFAR)
 Evaluating ways to apply wireless technology in
stores. Testing emerging RFID smart-tag systems, to
replace bar codes with a more efficient producttracking mechanism.
Reference - Power (2008)
Advanced Scout
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IBM has prototyped software to help National Basketball Association
(NBA) coaches and league officials organize and interpret the data
collected at every game. Using software called Advanced Scout to
prepare for a game, a coach can quickly review countless stats:
shots attempted, shots blocked, assists made, personal fouls. But
Advanced Scout can also detect patterns in these statistics that a
coach may not have known about. Advanced Scout software
provides an easy and meaningful way to process information. "It
helps coaches easily mine through and analyze a lot of data and no
computer training or data analysis background is required," says Dr.
Inderpal Bhandari, computer scientist at IBM's T.J. Watson
Research Center. Patterns found through analysis are linked to the
video of the game. Coaches can look at just those clips that make
up an interesting pattern.
FedEx Business Intelligence
System
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Federal Express, based in Memphis, Tenn., rolled out
Business Intelligence capabilities to a global base of 700
end-users. FedEx created a central, integrated data
warehouse hub, which provides Web-based, real-time
access to financial and logistical information necessary
for planning and decision-making. The solution, from
Pinnacle Solutions Inc., was deployed on a group of Dell
PowerEdge servers running Windows NT Server 4.0.
Data is stored in an Oracle database, and analytical
queries are run against a separate server running
Hyperion Essbase, an online analytical processing
(OLAP) engine. Most access is from browsers over the
corporate intranet, along with some standard
client/server deployments using Excel spreadsheets.
DSS Benefits
Improve personal efficiency
 Expedite problem solving and improve
decision quality
 Facilitate interpersonal communication
 Promote learning or training
 Increase organizational control
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Reference - Power (2008)
Other DSS Benefits
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Extending decision-makers’ ability to process information and
analyze it
Helping decision-makers deal with complex, large-scale problems
Decreasing the amount of time needed to make a decision, reducing
the decision cycle
Improving the reliability and enforcing the structure of a decision
process
Encouraging exploration and discovery by the decision-maker in
less structured or more novel decision situations related to the
domain or scope of the DSS;
Creating a competitive or strategic advantage for an organization.
Some DSS development opportunities are better than others.
Reference - Power (2008)
Risks
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Gaining any advantage may require large
financial investments
Competitors’ responses may result in a
heated race to gain or regain market share
Technology risks include:
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Picking the wrong vendor, using new technology
too early in technology life cycle, and using a
technology that might soon become obsolete
Reference - Power (2008)
Risks
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People cause the greatest risk
 Inability
to predict human behaviors and
reactions
 Basic human instinct to resist change
 Power struggles
 Personal motives
* No matter how wonderful a proposed DSS, if people resist the
change the system fails
Reference - Power (2008)
Questions for Further Thought
Do managers need the support provided
by DSS?
 Do managers want to use DSS?
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