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Transcript
INTRO TO MANAGEMENT
SUPPORT SYSTEMS
IS 340
BY
CHANDRA S. AMARAVADI
IN THIS PRESENTATION..
Introduction to MSS
 Decisions & types of decisions
 DSS
 EIS
 GDSS

2
INTRO TO MSS
3
INTRODUCTION (FYI)
 More competition
 Globalization
 Complexity
More decision making (D.M)
4
MANAGEMENT SUPPORT SYSTEMS
MSS: collection of tools/systems to support
managerial activity.
Characteristics (FYI):
 Interactive
 Customizable
 Model based
 Support rather than automate
5
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
6
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?
7
TYPES OF DECISIONS
When to produce?
What products?
Types of Decisions
Structured problem
(routine)
Unstructured problem
(non-routine)
8
DECISION MAKING STYLES
Unstructured
Structured
D.M. Styles
Analytical
{focus on methods &
models}
Intuitive
{focus on cues,
trial & error}
9
THE IDC MODEL OF DECISION MAKING
Intelligence
Design
Choice
Decision !
10
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
11
DECISION SUPPORT SYSTEMS
12
DECISION SUPPORT SYSTEMS
A system that supports structured and semi-structured
decision making by managers in their own
personalized way.
13
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
14
DSS ANALYSIS CAPABILITIES

“What - if “

Sensitivity

Goal-seeking

Optimization
15
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
16
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.
17
BANK EXAMPLE
Tellers
Que1
Que2
Tellers
Tellers
Que3
Arrival of Customers Waiting
Customers
Que4
Departure of
Customers
18
SIMULATION MODEL
Customer Arrives
PURPOSE: Identify #
of tellers needed, service
time
Joins Que
Is processed
Customer leaves
19
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)
20
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?
21
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?
22
DECISION OPTIONS (FYI)
Options are
Kuniang (w crane),
Kuniang (no crane),
General dynamics vessel, or
tug barge
23
DATA FOR THE 4 OPTIONS (FYI)
General
Dynamics
Tug
Barge
Kuniang
Kuniang
(Gearless) (Self-loader)
Capital cost
$70 mil.
$32 mil
Bid+$15mil
Bid+$36mil
Capacity
36,250 tons
30,000 tons
45,750 tons
40,000 tons
Round trip (coal)
5.15 days
7.15 days
8.18 days
5.39 days
Round trip (Egypt)
79 days
134 days
90 days
84 days
Operating cost/day
$18,670
$12,000
$23,000
$24,300
Fixed cost/day
$2,400
$2,400
$2,400
$2,700
Revenue/trip coal
$304,500
$222,000
$329,400
$336,000
Revenue/trip Egypt
$2,540,000
$2,100,000
$3,570,000
$2,800,000
24
DECISION TREE OF HOW MUCH TO BID
Decision
Outcome
0.7
Salvage=scrap
0.5
Win
?
Salvage=bid
Bid $7mil
Total
Cost
NPV
Self-Unloader
43
-1.35
Gearless
Self-Unloader
22
5.8
43
-1.35
Gearless
28
3.2
Sister Ship
2.1
Tug/Barge
-0.6
Lose
Note: NPV calculations are based on projections from previous slide
25
 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
26
DSS APPLICATIONS







Cash forecasting
Fire-fighting
Portfolio selection
Evaluate lending risk
Event scheduling
School location
Police beat
27
DATA MINING
28
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
29
 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
30
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
31
BI SYSTEMS
(ALSO EXECUTIVE
INFORMATION SYSTEMS)
32
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?
33
BI ARCHITECTURE
Medline
FedStats
BI Workstation
OLAP/
WAREHOUSE
Costs: $50,000 - $100,000
Development time: about 1 month
Internal
Databases
34
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
EXAMPLES
EXAMPLES..
COLLABORATIVE SYSTEMS
(GDSS)
38
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.
39
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?
40
CHARACTERISTICS OF GROUP D.M.




Participants of equal rank
5-20
Time limits
Requires knowledge from participants
41
A GROUP DECISION SUPPORT
SYSTEM
Screen
Database
Org
Memory
A GDSS System
A repository of the
D.M. process.
42
GROUP DECISION SUPPORT SYSTEMS
43
GDSS THEORY
Process
losses
-
GDSS
+
Process
gains
A GDSS minimizes process losses and maximizes
process gains
44
ADVANTAGES OF GDSS
 Time
 Anonymity
 Democratic participation
 Satisfaction
 Record of decision
45
THE END
46