Title: INTRO%20TO%20MANAGEMENT
1INTRO TO MANAGEMENT SUPPORT SYSTEMS IS
340 BY CHANDRA S. AMARAVADI
2IN THIS PRESENTATION..
- Introduction to MSS
- Decisions types of decisions
- DSS
- EIS
- GDSS
3INTRO TO MSS
4INTRODUCTION (FYI)
- More competition
- Globalization
- Complexity
More decision making (D.M)
5MANAGEMENT SUPPORT SYSTEMS
MSS collection of tools/systems to support
managerial activity.
Characteristics (FYI)
- Interactive
- Customizable
- Model based
- Support rather than automate
6MANAGEMENT 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
7EXAMPLES 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?
8TYPES OF DECISIONS
When to produce?
What products?
Types of Decisions
Structured problem (routine)
Unstructured problem (non-routine)
9DECISION MAKING STYLES
Unstructured
Structured
D.M. Styles
Analytical
Intuitive
focus on methods models
focus on cues, trial error
10THE IDC MODEL OF DECISION MAKING
Intelligence
Design
Choice
Decision !
11THE 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
12DECISION SUPPORT SYSTEMS
13DECISION SUPPORT SYSTEMS
A system that supports structured and
semi-structured decision making by managers in
their own personalized way.
14CLASSICAL 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
15DSS ANALYSIS CAPABILITIES
- What - if
- Sensitivity
- Goal-seeking
- Optimization
16DSS 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
17DSS 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.
18BANK EXAMPLE
Tellers
Tellers
Tellers
Que1 Que2 Que3 Que4
Arrival of Customers
Departure of Customers
Customers Waiting
19SIMULATION MODEL
PURPOSE Identify of tellers needed, service
time
Customer Arrives
Joins Que
Is processed
Customer leaves
20CASE 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)
21NEW 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?
22DECISION 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?
23DECISION OPTIONS (FYI)
Options are
- Kuniang (w crane),
- Kuniang (no crane),
- General dynamics vessel, or
- tug barge
24DATA 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
25DECISION 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
26CONCLUSIONS (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
27DSS APPLICATIONS
- Cash forecasting
- Fire-fighting
- Portfolio selection
- Evaluate lending risk
- Event scheduling
- School location
- Police beat
28DATA MINING
29DATA 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
30SOME 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
31DATA 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
32BI SYSTEMS (ALSO EXECUTIVE INFORMATION SYSTEMS)
33BI 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?
34BI ARCHITECTURE
Medline
FedStats
BI Workstation
OLAP/ WAREHOUSE
Internal Databases
Costs 50,000 - 100,000 Development time about
1 month
35BI 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
36EXAMPLES
37EXAMPLES..
38COLLABORATIVE SYSTEMS (GDSS)
39COLLABORATIVE 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.
40CURRENT 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?
41CHARACTERISTICS OF GROUP D.M.
- Participants of equal rank
- 5-20
- Time limits
- Requires knowledge from participants
42A GROUP DECISION SUPPORT SYSTEM
Screen
Database
Org Memory
A GDSS System
A repository of the D.M. process.
43GROUP DECISION SUPPORT SYSTEMS
44GDSS THEORY
Process losses
Process gains
-
GDSS
A GDSS minimizes process losses and
maximizes process gains
45ADVANTAGES OF GDSS
- Time
- Anonymity
- Democratic participation
- Satisfaction
- Record of decision
46THE END