1. MANAGEMENT SUPPORT SYSTEMS
PART I
IS524 BY
CHANDRA S. AMARAVADI

2. IN THIS PRESENTATION.. Introduction to MSS
Decisions & types of decisions
DSS
BI/EIS
Collaborative systems
Social networking

3. INTRO TO MSS

4. INTRODUCTION The complexity of business is increasing by the day:
More competition
More products/services
More promotions/payment options
Globalization
More decision making (D.M)

5. EVOLUTION OF MSS
MSS
GIS
problems led to
Collab-
orative
GDSS TP
Reporting
DSS
BI/EIS
Dbms
are back
ends for DW
Note:
DSS – Decision Support Systems, BI – Business Intelligence , DW – Data Warehouse,
EIS – Executive Information Systems, GIS – Geographical Information systems,
GDSS – Group Decision Support Systems, MSS – Management Support Systems.

6. MANAGEMENT SUPPORT SYSTEM
MSS: collection of tools/systems to support managerial activity.
Characteristics:
intended for all types of decisions*
interactive
customizable
some MSS are model based
What does managerial activity mean?
*decisions have to be non-trivial

7. EXAMPLES OF DECISIONS Whether to approve a loan?
structured
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?
Design a training program for employees.
What products to produce? What markets?
What production techniques to use?
How to deal with reducing sales?
Types of Decisions
un-structured

8. DECISION MAKING STYLES
Intuitive
Analytical
focus on cues, trial & error.
focus on methods & models.
D.M. Styles
Unstructured
Structured
Decision making tends to be individualistic

9. INDIVIDUAL MODEL OF D.M.(IDC)
Steps in D.M. for individual/group
(rational model)
Intelligence
Design
Choice
Decision !

10. THE IDC MODEL OF DECISION MAKING
Introduced by Herbert Simon, the IDC models steps in
individual (and rational) decision making and consists of stages:
Intelligence -- Identification of problem and constraints
Design Identification of alternative solutions
Choice Choosing a solution which optimizes D.M.
criteria

11. ORGANIZATIONAL MODELS OF D.M
Overall method of D.M. employed by organizations
Rational model
Bureaucratic model
Political model
Garbage can

12. ORGANIZATIONAL MODELS OF D.M
Rational model
Decisions made for rational reasons
Maximization of utility
Bureaucratic model
Decisions made under complex set of constraints
Multiple parties involved; extended time periods
Satisficing (sub-optimal solutions)
Political model
D.M. subject to politics
Maximize utility of particular stakeholders
Survival of the strongest! The “power maxim”
Garbage can
Problems and solutions coming together unexpectedly!

13. NO IDEAL DECISIONS BECAUSE
Bounded rationality
human cognitive limitations
Anchoring and adjustment biases
estimates based & adjusted on recent values
Localized search/Uncertainty avoidance
search for solutions in known vicinities.
prefer alternative which is more certain
Heuristics
substitute to the d.m. process
Constraints
if alternatives are large and time constraints

14. DECISION SUPPORT SYSTEMS

15. DECISION SUPPORT SYSTEMS
A system that supports structured and semi-structured decision making by managers in their own personalized way e.g. to decide projects, investments etc.

16. DSS CONCEPTS Dialogue Management Decision Parameters Decisions
Int rate = 5%
Growth = 10%
IRR = 15%
Dialogue Management
Decision Parameters
Decisions
Model Management
Decision Model
Decision Utility
Data Management
Decision Data
Decision Support System

17. CLASSICAL DSS ARCHITECTURE
Dialog Management
Model Management
Data Management
Depicts architecture or components of a DSS.

18. DSS BASIC CONCEPTS Decision Decision parameter Decision model
Description
Decision
Choice made in a business situation.
Decision parameter
Variable in a decision such as revenue growth. Also called decision variable.
Decision model
Abstract representation of a decision e.g. NPV or LP model.
Decision data
Data used in the decision, example data on inventory levels.
Decision utility
What outcomes are valued by D.M. e.g. reduce costs, increase sales etc.
Dialog management
The user interface through which the problem is entered.
Model management
The interface that supports creation and linking of models to decision parameters.
Data management
Facilities to manage decision data e.g. sort/filter data.

19. DSS ANALYSIS CAPABILITIES
These can be thought of as problem solving modes in a DSS or what a DSS can do.
“What - if “
Sensitivity
Goal-seeking
Optimization

20. DSS ANALYSIS CAPABILITIES
What if - change one or more variables. What if sales were to increase by 10% instead of 15%?
Sensitivity Change one variable such as int. rates or growth rate
Goal seeking - finding a solution to satisfy constraints. What level of sales would satisfy profit target?
Optimization- find best solution under a given set of constraints. What level of sales would maximize profit?

21. DSS MODELS

22. CLASSIFICATION OF MODELS
DSS Models are classified based on level of uncertainty as follows:
DECISION MAKING UNDER CERTAINTY
Linear programming (LP)
Integer Programming
Non-linear programming models
Graph models (e.g. PERT)
DECISION MAKING UNDER RISK
Decision tree
Bayesian Analysis
Queing, Discrete Event Simulation
Markov

23. CLASSIFICATION OF MODELS
DECISION MAKING UNDER UNCERTAINTY
Causal models
Influence diagrams
Strategic Assumption Surfacing & Testing (SAST)

24. LINEAR PROGRAMMING MODEL
An aircraft manufacturing company needs to decide how many
of two-engine (a2), three-engine (a3) and four-engine (a4) models
to produce (monthly) in order to maximize its profits. The profit
contributions and constraints are as follows:
Profit contribution -- a2: $500,000 a3: 650,000 a4:900,000
a2, a3, a4 require two, three and four engines respectively
maximum # of available engines are 60 per month
a2, a3, a4 require 135, 175 and 245 seats respectively
there is a limit of 3500 seats per month
the plant capacity is limited to 30 aircraft of any type
Formulate an LP model of the situation above.

25. This slide is intentionally blank.

26. D.M.UNDER RISK CASE OF SS.KUNIANG
Ship ran aground
Owners wanted to sell it
Sealed bid
Coast guard was the authority
Scrap value ($5m)
Repair cost ($15m)

27. 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?

28. DECISION COMPLICATIONS
Type of coal: PA or Egypt?
Jones Act and round trip time
Exception to Jones Act
Coast guard valuation
Self unloader?
reduces cargo capacity
Buy a sister vessel? Tug barge?

29. DECISION OPTIONS Options are Kuniang (w crane), Kuniang (w.out crane),
General dynamics vessel, or
tug barge

30. DATA FOR THE 4 OPTIONS General Tug Kuniang Kuniang Dynamics Barge
(Gearless)
(Self-loader)
Capital cost
Capacity
Round trip (PA)
Round trip (Egypt)
Operating cost/day
Fixed cost/day
Revenue/trip PA
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
Bid+$15mil
45,750 tons
8.18 days
90 days
$23,000
$2,400
$329,400
$3,570,000
Bid+$36mil
40,000 tons
5.39 days
84 days
$24,300
$2,700
$336,000
$2,800,000

31. DECISION TREE OF HOW MUCH TO BID
Total
Decision
Outcome
Cost
NPV
0.7
Value =scrap
Self-Unloader 43 22
-1.35
5.8
2.1
-0.6
0.5
Win
Gearless ?
Self-Unloader
Value =bid
Gearless
Bid $7mil
Sister Ship
Lose
Tug/Barge
Note: NPV calculations are based on projections from previous slide

32. CONCLUSIONS
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
Under what conditions is the decision tree approach useful?

33. CAUSAL MODELS* A model that depicts causality i.e. causes & effects +
Used to depict complex decision situations. +
earnings
stock
Price -
costs
‘+’ : is proportional
‘-’ : is inversely proportional
*Also known as cause maps

34. DISCUSSION Depict the relationship between development of society,
economy, technology and jobs using causal maps. What
assumptions are relevant here?

35. ANALYSING A DECISION SITUATION
THE KURSK TRAGEDY
Russian submarine named after Kursk city
Oscar II class, largest nuclear attack sub
sank in Barents sea in Aug 2000
naval exercise firing torpedos
inferior quality torpedos
defective weld
explosion
oxygen regeneration unit problem
second explosion killed most of crew
23 sailors remaining for 15 days
Norway and Britain offer help

36. DSS APPLICATIONS & TRENDS

37. DSS APPLICATIONS (FYI)
These are some examples of DSS applications:
Cash forecasting
Fire-fighting
Portfolio selection
Evaluate lending risk
Event scheduling
School location
movie forecasting (movie guru)

38. EXTENSIONS TO DSS The DSS concept has been extended as follows:
BI systems (formerly EIS)
Geographical Information Systems (GIS)
Collaborative Systems (extension of GDSS)
Social networking
Expert Systems
Data mining/warehousing

39. (AKA EXECUTIVE INFORMATION SYSTEMS)
BI SYSTEMS
(AKA EXECUTIVE INFORMATION SYSTEMS)

40. BI SYSTEM
BI System: Collection of technologies for presenting business information to decision makers.
presented via a “dashboard”
an intuitive, easy-to-navigate, graphical display
customizable
information from multiple sources, departments, or markets
automatic updates (if possible)

41. BI ARCHITECTURE Dashboard OLAP Sales data Ware- house Accounting data
Production
data

42. BI CAPABILITIES
Drill down
Score cards/metrics
Trend analysis
Reports

43. BI TECHNOLOGIES Data warehouses (in MSS II)
Cube/Multi-dimensional organization
Aggregation
Histogram, Bar, Pi, box plots (not discussed)

44. AGGREGATION & CUBE sales in the Northern region? NE SE SW 65 20 30 40
Units
North E. 40
South E. 20
South W. 30
Midwest 65 80 NE SE SW 65 20 30 40 50 80
sales in the Northern region?

45. DIMENSIONS & CONCEPT HIERARCHIES
A dimension is an aspect of the data, it is a characteristic of a variable such as location, for sales variable.
Dimensions can have hierarchies (or various levels of aggregations)
A concept hierarchy defines a sequence of mappings from a set of low-level concepts to higher-level, more general concepts

46. CONCEPT HIERARCHY Mfr. dimension GAL S7 GAL S6 G5 G4 G3 48.7 mil
Iphone 7
Iphone 6
48.7 mil
GAL S7
GAL S6 G5 G4 G3
10 mil
38 mil
15,000
11 mil
10 mil

47. MULTI-DIMENSIONAL ORGANIZATION
Sales, costs etc. nw
Products
(tables, desks, lamps..) sw
Regions
Cube organization supports slice & dice

48. MULTI-DIMENSIONAL ORGANIZATION..LG MW SW SE NW NE
TOTAL G5
3,780
4,893
7,494
6,520
2,450
25,137 G4
2,342
1,200
1,400
1,678
950
7,570 G3
7,893
5,647
6,493
7,839
31,652
9,902
13,986
14,541
14,691
11,239
64,359
March sales LG MW SW SE NW NE
TOTAL G5
3,780
4,893
7,494
6,520
2,450
25,137 G4
2,342
1,200
1,400
1,678
950
7,570 G3
7,893
5,647
6,493
7,839
31,652
9,902
13,986
14,541
14,691
11,239
64,359 LG MW SW SE NW NE
TOTAL G5
3,780
4,893
7,494
6,520
2,450
25,137 G4
2,342
1,200
1,400
1,678
950
7,570 G3
7,893
5,647
6,493
7,839
31,652
9,902
13,986
14,541
14,691
11,239
64,359
February sales
January sales
shows multi-dimensional/cube organization

49. CUBE ORGANIZATION Data from warehouse imported into memory
A sophisticated 3D representation is created
Referred to as “sparse matrix”
Sides of cube are dimensions
Allows “slice & dice”
Answers to high level queries/reports

50. DASHBOARD

51. DASHBOARD..

52. SCORE CARDS/METRICS

53. ANALYSIS

54. REPORTS

55. COLLABORATIVE SYSTEMS

56. COLLABORATIVE SYSTEMS
Technologies to support groups in achieving a
common goal:
strategic planning
develop reports
research
design
software development
Group decision Support Systems are a specialization of
collaborative systems.

57. COLLABORATIVE SYSTEMS
Collaborative systems provide support for:
Decision making (GDSS)
brainstorming, alternative eval., voting etc.
Document sharing
share documents e.g. Google docs, pbWorks, Domino
Design
web-based design environments
shared objects

58. COLLABORATIVE SYSTEMS
ADVANTAGES OF
COLLABORATIVE SYSTEMS
Supports the 22nd century organization
Reduces travel
Time savings
Improves end product (?)

59. SOCIAL MEDIA Definition: IT supported venues for social interaction
Example Twitter, Face book, Linked-in etc.
Useful when social system and work system interlinked
e.g. contacts on Linked-in, Gore-tex
Can be a valuable marketing tool
get leads – offers in social media
standing “ad” – company pages
ad to contacts -- example of iPhone
identify opinion leaders
sentiment analysis

60. DISCUSSION QUESTIONS What type of decision making is supported by DSS?
What would be examples of typical decisions supported by a DSS?
What is a decision model?
Can unstructured decision making be supported with models?
How can an Excel spreadsheet package be viewed as a DSS?
What types of D.M. styles are usually followed by executives?
Would an intuitive D.M. prefer a DSS or EIS why?
Why are collaborative systems increasingly being used?
What sort of features are provided by collaborative systems?
Does the quality of a product improve because of increased collaboration?
In what way is Social Networking an extension of Collaborative Systems?