INTRO%20TO%20MANAGEMENT

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INTRO TO MANAGEMENT SUPPORT SYSTEMS IS
340 BY CHANDRA S. AMARAVADI
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IN THIS PRESENTATION..

• Introduction to MSS
• Decisions types of decisions
• DSS
• EIS
• GDSS

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INTRO TO MSS
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INTRODUCTION (FYI)

• More competition
• Globalization
• Complexity

More decision making (D.M)
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MANAGEMENT SUPPORT SYSTEMS
MSS collection of tools/systems to support
managerial activity.
Characteristics (FYI)

• Interactive
• Customizable
• Model based
• Support rather than automate

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MANAGEMENT SUPPORT SYSTEMS
ES
GDSS
TP
Reporting
DSS
EIS
AI
DSS
Evolution
Data Mining
MSS
Note ES Expert Systems, AI Artificial
Intelligence EIS Executive Information Systems
DSS Decision Support Systems
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EXAMPLES OF DECISIONS

• Whether to approve a loan?
• Whether to promote an employee?
• How much of an increase to allocate to employees?
• Where to advertise? Allocation to media?
• How to finance a capital expansion project?
• How much to produce? When to produce?
• What products to produce? What markets?
• What production techniques to use?

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TYPES OF DECISIONS
When to produce?
What products?
Types of Decisions
Structured problem (routine)
Unstructured problem (non-routine)
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DECISION MAKING STYLES
Unstructured
Structured
D.M. Styles
Analytical
Intuitive
focus on methods models
focus on cues, trial error
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THE IDC MODEL OF DECISION MAKING
Intelligence
Design
Choice
Decision !
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THE IDC MODEL OF DECISION MAKING
Introduced by Herbert Simon, the IDC consists
of The following stages
Intelligence -- Identification of problem
information Design -- Identification of
alternative solutions Choice -- Choosing
a solution which optimizes D.M.
criteria
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DECISION SUPPORT SYSTEMS
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DECISION SUPPORT SYSTEMS
A system that supports structured and
semi-structured decision making by managers in
their own personalized way.
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CLASSICAL DSS ARCHITECTURE
Dialog management
User interface
Model management
Capabilities for creating linking models
Data management
Capabilities for managing accessing data
Database
Note model is an abstract representation of a
problem
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DSS ANALYSIS CAPABILITIES

• What - if
• Sensitivity
• Goal-seeking
• Optimization

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DSS ANALYSIS CAPABILITIES
What if - change one or more
variables Sensitivity - change one variable
Goal seeking - finding a solution to satisfy
constraints Optimization- find best
solution under a given
set of constraints
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DSS MODELS (FYI)

• Financial
• e.g. portfolio, NPV
• Statistical
• e.g. forecasting
• Marketing
• e.g. product mix, advertising
• Production
• e.g. capacity planning, inventory
• Simulation
• e.g. production process, bank tellers etc.

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BANK EXAMPLE
Tellers
Tellers
Tellers
Que1 Que2 Que3 Que4
Arrival of Customers
Departure of Customers
Customers Waiting
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SIMULATION MODEL
PURPOSE Identify of tellers needed, service
time
Customer Arrives
Joins Que
Is processed
Customer leaves
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CASE OF THE S.S. KUNIANG (FYI)

• Ship ran aground
• Owners wanted to sell it
• Coast guard was the authority
• Sealed bid
• Scrap value (5m)
• Repair cost (15m)

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NEW ENGLAND ELECTRIC SYSTEM

• Utility company needs coal
• 4m tons/year
• Purchased a 70m General Dynamics vessel
• Capacity 36,250 tons (self loading)
• Bid for Kuniang?
• How much?

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DECISION COMPLICATIONS

• Type of coal Egypt or PA?
• Jones Act and round trip time
• Exception to Jones Act
• Self unloader reduces cargo capacity
• Buy a sister vessel? Tug barge?

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DECISION OPTIONS (FYI)
Options are

• Kuniang (w crane),
• Kuniang (no crane),
• General dynamics vessel, or
• tug barge

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DATA FOR THE 4 OPTIONS (FYI)
General
Tug
Kuniang
Kuniang
Dynamics
Barge
(Gearless)
(Self-loader)
Capital cost Capacity Round trip (coal) Round
trip (Egypt) Operating cost/day Fixed
cost/day Revenue/trip coal Revenue/trip Egypt
70 mil. 36,250 tons 5.15 days 79
days 18,670 2,400 304,500 2,540,000
32 mil 30,000 tons 7.15 days 134
days 12,000 2,400 222,000 2,100,000
Bid15mil 45,750 tons 8.18 days 90
days 23,000 2,400 329,400 3,570,000
Bid36mil 40,000 tons 5.39 days 84
days 24,300 2,700 336,000 2,800,000
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DECISION TREE OF HOW MUCH TO BID
Total
Decision Outcome
Cost
NPV
0.7
Salvagescrap
Self-Unloader
43 22 43 28
-1.35 5.8 -1.35 3.2 2.1 -0.6
0.5
Win
Gearless
Self-Unloader
?
Salvagebid
Gearless
Bid 7mil
Sister Ship
Lose
Tug/Barge
Note NPV calculations are based on projections
from previous slide
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CONCLUSIONS (FYI)

• NEES ended up bidding 6.7 million for the
  Kuniang, but lost to a bid of 10 million
• Coast Guard valued ship as scrap metal
• Decision tree a useful tool parameters unknown

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DSS APPLICATIONS

• Cash forecasting
• Fire-fighting
• Portfolio selection
• Evaluate lending risk
• Event scheduling
• School location
• Police beat

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DATA MINING
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DATA MINING
Search for relationships and global patterns that
exist in large databases but are hidden in the
vast amounts of data.
e.g. sequence/association, classification, and
clustering
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SOME DATA MINING APPLICATIONS

• Predicting the probability of default for
  consumer loans
• Predicting audience response to TV advertisements
• Predicting the probability that a cancer patient
  will respond to radiation therapy.
• Predicting the probability that an offshore well
  is going to produce oil

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DATA MINING ANALYSES

• Associations
• activities/purchases that occur together e.g.
  bread and jam.
• Sequence
• Activities which occur after each other e.g. car
  and loan
• Classification
• An analysis to group data into classes e.g. pepsi
  and coke drinkers

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BI SYSTEMS (ALSO EXECUTIVE INFORMATION SYSTEMS)
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BI SYSTEMS DASHBOARDS
BI System Systems that provide information to
executives on the business environment.
Executive Dashboard An interface that displays
information needed to effectively run an
enterprise.
Does more information lead to better quality
decisions?
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BI ARCHITECTURE
Medline
FedStats
BI Workstation
OLAP/ WAREHOUSE
Internal Databases
Costs 50,000 - 100,000 Development time about
1 month
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BI CHARACTERISTICS

• An intuitive easy-to-navigate graphical display
• A logical structure for easy access
• Little or no user training is required
• Data displays that can be customized
• Regular and frequent automatic updates of
  dashboard information
• Information from multiple sources, departments,
  or markets can be viewed simultaneously

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EXAMPLES
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EXAMPLES..
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COLLABORATIVE SYSTEMS (GDSS)
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COLLABORATIVE SYSTEMS
An interactive computer based system which
facilitates solution of unstructured problems by
a set of D.M. working together as a group.
Other terms - GDSS, Electronic Meeting Systems.
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CURRENT BUSINESS TRENDS (FYI)

• More competition
• Shift towards flat/virtual organizations
• More mergers industry consolidations
• Globalization of markets and products
• More strategic alliances

Group D.M.
Is it necessary for org. decisions to be made in
groups? Why cannot it be handled by individuals?
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CHARACTERISTICS OF GROUP D.M.

• Participants of equal rank
• 5-20
• Time limits
• Requires knowledge from participants

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A GROUP DECISION SUPPORT SYSTEM
Screen
Database
Org Memory
A GDSS System
A repository of the D.M. process.
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GROUP DECISION SUPPORT SYSTEMS
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GDSS THEORY
Process losses
Process gains
-

GDSS
A GDSS minimizes process losses and
maximizes process gains
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ADVANTAGES OF GDSS

• Time
• Anonymity
• Democratic participation
• Satisfaction
• Record of decision

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THE END