Chapter 9 e-commerce systemsMIS- MBA-1st Trimester- MBS

1. Chapter 10
Supporting
Decision Making

2. Introduction
• Companies are investing in new data-driven decision support
application frameworks that help them respond rapidly
– to the changing market conditions and customer needs
• To succeed, companies need IS that can support the
information and decision making needs of managers and
business professionals
• Internet, intranet and other web-enabled information
technologies have significantly strengthened the role of IS in
supporting the decision making activities
• This is accomplished by several types of IS
– Management information systems
– Decision support systems
– Other information systems

3. Information, Decisions & Management
Type of information reqd. by decision makers is directly
related to the level of management decision making &
the amount of structure in the decision situation

4. Levels of managerial decision making
Strategic
Management
Board of directors, executive committee
Develop organizational goals, strategies, policies
Monitor the strategic performance
Monitor political, economic & competitive environment
Tactical
Management
Professionals in self-directed teams, BU managers
Develop short and medium term plans, schedules & budgets
Specify sub-unit level policies, procedures & goals
Allocate resources & monitor their performance
Operational
Management
Members of self-directed teams, operating managers
Develop short term plans – weekly production schedules
Direct the use of resources
Direct the performance a/c to procedures, budget & schedules

5. Information Quality
• Information products are made more valuable by
their attributes, characteristics, or qualities
– Information that is outdated, inaccurate, or
hard to understand has much less value
• Information has 3 dimensions
– Time
– Content
– Form

6. Attributes of Information Quality

7. Decision Structure
• A way to understand decision making
– Structured or Operational decision making
– Semi-structured or Tactical decision making
– Unstructured or Strategic decision making

8. Structured and Operational
• The procedures to follow when decision is needed can be
specified in advance
• Nearly all variable are known
• Operational information systems can be programmed to
make operational decisions automatically
• Repetitive and Routine decision making
– How many workers are needed to staff Line A to meet our delivery
deadlines?
– What’s the optimal order quantity of Raw Material X, based on our
current production?
– What if production increases by 10%?
– When should we re-stock our inventory of Nike Air Max running shoes?
• Reduces/eliminates human involvement
• Increases efficiency

9. Semi-structured and Tactical
• Some decision procedures can be pre-specified,
but not enough to lead to the correct decision
• Partially programmable
• Human judgment required
• Degree of decision-making skill is required
• Some analogous history to guide decision maker, but not
enough to determine the optimal solution
– Which stock portfolio will yield the highest returns over the next 3
years?
– Hiring decisions, “lease vs. buy”?
– Optimum production level?
– Loan application evaluations – Work Bench SCBNL
– What’s the best advertising campaign to launch a new product?

10. Unstructured and Strategic
• Not possible to specify in advance most of the
decision procedures to follow
• Decision factors are extremely ambiguous and
complex
• Novel – no history to guide decision maker
• Extremely high degree of uncertainty
• No way to determine the optimal course of action
• Might as well guess!
– Should we stock Japanese-inspired evening gowns?
– Company reorganization
– Installing a new plant in a factory

12. Information Systems
A mathematical modeling technique used for simulating, explaining, and making
predictions. A model may help to explain a system and to study the effects of different
components, and to make predictions about behavior.

13. Trends in Decision Support
• The emerging class of applications focuses on
– Personalized decision support
– Modeling
– Information retrieval
– Data warehousing
– What-if scenarios
– Reporting

14. Decision Support Systems
Decision Support Systems are computer based
information systems that provide interactive information
support to managers and business professionals during
decision making process.
• Decision support systems use the following to support the
making of semi-structured business decisions
– Analytical models
– Specialized databases
– A decision-maker’s own insights and judgments
– An interactive, computer-based modeling process
• DSS systems are designed to be ad hoc, quick-response
systems that are initiated and controlled by decision makers

15. DSS
Components

16. Applications of Statistics and Modeling
– Supply Chain: simulate and optimize supply
chain flows, reduce inventory, reduce stock-outs
– Pricing: identify the price that maximizes
yield or profit
– Product and Service Quality: detect quality
problems early in order to minimize them
– Research and Development: improve quality,
efficacy, and safety of products and services

17. DSS Model Base
• Model-driven DSS use algebraic, decision analytic,
financial, simulation, and optimization models to provide
decision support.
• Model Base
– A software component that consists of models used in
computational and analytical routines that
mathematically express relations among variables
• Spreadsheet Examples
– Linear programming
– Multiple regression forecasting
– Capital budgeting present value

18. Management Information Systems
• The original type of information system
that supported managerial decision making
– Produces information products that support
many day-to-day decision-making needs
– Produces reports, display, and responses
– Satisfies needs of operational and tactical decision
makers who face structured decisions

19. Management Reporting Alternatives
• Periodic Scheduled Reports
– Prespecified format on a regular basis
• Exception Reports
– Reports about exceptional conditions
– May be produced regularly or when an
exception occurs
• Demand Reports and Responses
– Information is available on demand
• Push Reporting
– Information is pushed to a networked computer

20. Online Analytical Processing (OLAP)
• IS that can interactively provide rapid response to
complex business queries online in real time
• Enables managers and analysts to interactively examine
and manipulate large amounts of detailed and
consolidated data from many perspectives
• Analyzes complex relationships among thousands and
millions of data items stored in various databases to
discover patterns, trends and exceptions conditions
• Cluster of analytical databases, data marts, data
warehouses, data mining techniques and multidimensional
database structures with web-enabled software products

21. Online Analytical Operations
• Consolidation
– Aggregation of data or complex groupings involving
interrelated data
– Ex: data about sales offices rolled up to the district level
• Drill-Down
– Display underlying detail in consolidated data
– Ex: sales figures by each product or reps that make up a
region’s sales totals
• Slicing and Dicing
– Viewing database from different viewpoints
– Often performed along a time axis
– Ex: All sales of a product type within regions
All sales by sales channel within each product type

22. Using Decision Support Systems
• Using a decision support system involves an interactive analytical modeling
process
– Decision makers are not demanding pre-specified information
– They are exploring possible alternatives
• What-If Analysis
– Observing how changes to selected variables affect other variables
– Cut advertising by 10% -> What will happen to sales?
• Sensitivity Analysis
– Observing how repeated changes to one variable affect other variables
– Cut advertising repeatedly by $100 -> what is relationship to sales?
• Goal-seeking Analysis
– Making repeated changes to selected variables until a chosen variable reaches
a target value
– Increase advertising until sales reaches $1000
• Optimization Analysis
– Finding an optimum value for selected variables, given certain constraints
– Given the budget and choice of media-> what’s the best advertising plan?

23. Data Mining
• Vital tool for organizing and exploiting the data resources
of a company
• Provides decision support through knowledge discovery
– Analyzes vast stores of historical business data
– Looks for patterns, trends, and correlations
– Goal is to improve business performance
• Types of analysis
– Regression
– Decision tree
– Neural network
– Cluster detection
– Market basket analysis

24. Data mining Applications
• Highlighting patterns
• Reveal customer tendencies
• Cut redundant costs
• Uncover unseen profitable relationships and
opportunities
– Successful direct mailing
– Discover better ways to display product
– Design a better e-commerce website
– Reach untapped profitable customers
– Recognize unprofitable customers or products
– Data-driven marketing with MBA

25. Executive Information Systems
• Combines many features of MIS and DSS
• Provide top executives with immediate and
easy access to information about CSFs
• Identify factors that are critical to
accomplishing strategic objectives (critical
success factors)
• So popular that it has been expanded to
managers, analysis, and other knowledge
workers

26. Features of an EIS
• Information presented in forms tailored to the
preferences of the executives using the system
– Customizable graphical user interfaces
– Exception reports
– Trend analysis
– Drill down capability

27. Enterprise Information Portals
• An EIP is a Web-based interface and integration
of MIS, DSS, EIS, and other technologies
– Available to all intranet users and select
extranet users
– Provides access to a variety of internal and external
business applications and services
– Typically tailored or personalized to the user
or groups of users
– Often has a digital dashboard
– Also called enterprise knowledge portals

28. Dashboard Example

29. Enterprise Information Portal Components

30. Enterprise Knowledge Portal

31. Self study

32. Expert Systems
• An Expert System (ES)
– A knowledge-based information system
– Contain knowledge about a specific, complex
application area
– Acts as an expert consultant to end users

33. Components of an Expert System
• Knowledge Base
– Facts about a specific subject area
– Heuristics that express the reasoning procedures of an
expert (rules of thumb)
• Software Resources
– An inference engine processes the knowledge
and recommends a course of action
– User interface programs communicate with
the end user
– Explanation programs explain the reasoning process to
the end user

34. Components of an Expert System

35. Methods of Knowledge Representation
• Case-Based
– Knowledge organized in the form of cases
– Cases are examples of past performance,
occurrences, and experiences
• Frame-Based
– Knowledge organized in a hierarchy or
network of frames
– A frame is a collection of knowledge about
an entity, consisting of a complex package
of data values describing its attributes

36. Methods of Knowledge Representation
• Object-Based
– Knowledge represented as a network of objects
– An object is a data element that includes both
data and the methods or processes that act on
those data
• Rule-Based
– Knowledge represented in the form of rules
and statements of fact
– Rules are statements that typically take the
form of a premise and a conclusion (If, Then)

37. Expert System Application Categories
• Decision Management
– Loan portfolio analysis
– Employee performance evaluation
– Insurance underwriting
• Diagnostic/Troubleshooting
– Equipment calibration
– Help desk operations
– Medical diagnosis
– Software debugging

38. Expert System Application Categories
• Design/Configuration
– Computer option installation
– Manufacturability studies
– Communications networks
• Selection/Classification
– Material selection
– Delinquent account identification
– Information classification
– Suspect identification

39. Expert System Application Categories
• Process Monitoring/Control
– Machine control (including robotics)
– Inventory control
– Production monitoring
– Chemical testing

40. Benefits of Expert Systems
• Captures the expertise of an expert or group
of experts in a computer-based information
system
– Faster and more consistent than an expert
– Can contain knowledge of multiple experts
– Does not get tired or distracted
– Cannot be overworked or stressed
– Helps preserve and reproduce the knowledge
of human experts

41. Limitations of Expert Systems
• The major limitations of expert systems
– Limited focus
– Inability to learn
– Maintenance problems
– Development cost
– Can only solve specific types of problems
in a limited domain of knowledge