Title: Supporting Business Decision-Making
1Supporting Business Decision-Making
- Good Information is Essential for Fact-Based
Decision-Making
2The 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
- Decisions today determine the landscape of
tomorrow's world
3Decision 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
4Classifying Decisions
- Functional area
- Finance decisions
- Marketing decisions
- Production decisions
- Personnel decisions, etc.
- Managerial Function
- Planning decisions
- Organizing decisions
- Control decisions, etc.
5Classifying Decisions
- Management Level
- Strategic decisions
- Tactical decisions
- Operational decisions
- Structure of decision
- Structured/Programmed decisions
- Semi-structured decisions
- Unstructured decisions
6Decision Support System Definition
- 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 - 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). - DSS are ancillary or auxiliary systems they are
not intended to replace skilled decision-makers
Reference - Power (2008)
7DSS 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
Reference - Power (2008)
8MIS and DSS Brief History
- 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)
9DSS History - Specifics
- 1951 Lyons Tea Shops used LEO 1 digital computer
to factor in weather forecasts to determine what
fresh produce delivery vans would carry to
Lyons 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 PG built a DSS that linked sales
information and retail scanner data
Reference - Power (2008)
10DSS Conceptual Perspective
- 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 - Business intelligence, knowledge management
Reference - Power (2008)
11Characteristics of DSS
- 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)
12Characteristics 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
Reference - Power (2008)
13Is a DSS an MIS?
- 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)
14Transaction 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
Reference - Power (2008)
15DSS vs. Transaction Processing Systems (TPS)
- 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)
16DSS Applications
- 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
17Alters Categories of DSS
- Data-Driven
- File Drawer Systems
- Data Analysis Systems
- Analysis Information Systems
- Model-Driven
- Accounting and Financial Models
- Representational Models
- Optimization Models
- Knowledge-Driven
- Suggestion Models
Reference - Power (2008)
18Alters Categories of DSS
- Data-Driven
- File Drawer Systems
- Data Analysis Systems
- Analysis Information Systems
Reference - Power (2008)
19Alters Categories of DSS
- Model-Driven
- Accounting and Financial Models
- Representational Models
- Optimization Models
Reference - Power (2008)
20Alters Categories of DSS
- Knowledge-Driven
- Suggestion Models
Reference - Power (2008)
21Framework
- Primary framework dimension is the dominant
component or driver of the decision support
system (Power, 2002) - Secondary dimensions are
- The intended or targeted users,
- The specific purpose of the system
- The primary deployment or enabling technology
Reference - Power (2008)
22Identify 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
Reference - Power (2008)
23DSS Framework
- 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)
24DSS Framework
- Data-driven
- Includes File Drawer/Management Reporting, Data
Warehousing and Analysis Systems, Executive
information Systems (EIS), and Geographic
Information Systems external data - 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)
25DSS Framework
- Knowledge-driven
- Built using AI tools, data mining tools and
management expert systems - Model-driven
- Include systems that use accounting and financial
models, representative models, and optimization
models - Emphasize access to and manipulation of a model,
Whit If? analysis
Reference - Power (2008)
26DSS Framework
- Intended Users, e.g. Inter-Organizational DSS
- Designed for customers and suppliers
- Data, model, document, knowledge, or
communications-driven - 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)
27Describing a Specific DSS
- 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)
28Enabling 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
Reference - Power (2008)
29Building DSS - components
- Database Component
- Knowledge
- Data
- Documents
- Model Component
- Interface Engine
- Models
- External Data
- Dow Jones
- Reuters
- Communications Component
- DSS Architecture
- Network
- Web server
- Client/Server
- Mainframe
- Internal Data
- Personnel
- Production
- Finance
- Marketing
- User Interface Component
- Dialog
- Maps
- Menus, Icons
- Representations
- Charts, graphs
- Web Browser
Users
Reference - Power (2008)
30Building DSS User Interface
- User Interface
- Most Important Component
- Tools needed
- DSS Generator
- Query Reporting Tools
- Front-End Development Packages
Reference - Power (2008)
31Building DSS Database
- 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)
32Building DSS Models
- 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)
33Building DSS Architecture
- 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)
34Challenges of DSS
- 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)
35Gaining Competitive Advantage
- 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
36How 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
37How 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
38Strategic DSS Examples
- Frito-Lay
- L.L. Bean
- Lockheed - Georgia
- Mrs. Fields Cookies
- Wal-Mart
- A company needs to continually invest in a
Strategic DSS to maintain any advantage.
Classic examples!!
Reference - Power (2008)
39Frito-Lay
- Route Sales people were all given a hand-held
computer - Enables sales people to have decision-making role
- Allows Frito-Lay to track products
- The data is put into a Data-Driven DSS
- Automated a cumbersome process and improved the
quality of data
Reference - Power (2008)
40L.L. Bean
- 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)
41Mrs. Fields Cookies
- Developed MIS in early 1980s to provide
uniformity in store management also supporting
rapid expansion - Designed to serve two purposes
- Control and better management decision-making
- Enabled each store to be run as Debbie Field ran
the original store
Reference - Power (2008)
42Mrs. Fields Cookies
- 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)
43Wal-Mart
- 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
product-tracking mechanism.
Reference - Power (2008)
44Advanced Scout
- 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.
45FedEx Business Intelligence System
- 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.
46DSS Benefits
- Improve personal efficiency
- Expedite problem solving and improve decision
quality - Facilitate interpersonal communication
- Promote learning or training
- Increase organizational control
Reference - Power (2008)
47Other DSS Benefits
- 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)
48Risks
- 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
- 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)
49Risks
- 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)
50Questions for Further Thought
- Do managers need the support provided by DSS?
- Do managers want to use DSS?