Transcript Info Systems for Managerial Decision Support

Information Systems for
Managerial Decision Support
 Introduction
 Information, Decisions, and Management
 Decision Support Technologies
 OLAP and DSS
 DSS Applications in Corporate Functional Management
 Practitioners of Management Science
Achieving Success with Analytics
Optimization Modeling
Predictive Modeling
What is the best that
could happen?
Descriptive
Modeling
$ROI
Ad Hoc Reports
and OLAP
What will happen?
Standard
Reports
Raw Data
Why did it happen?
What happened?
Data
Information
Decision Support
Intelligence
Decision Guidance
Introduction

Management
– A process by which organizational goals
are achieved through the use of resources
 Resources:
Inputs
 Goal Attainment: Output
 Measuring Success:
Productivity = Outputs / Inputs
Introduction cont.
Management is decision making
The manager is a decision maker
Now fast changing, complex environment
Factors affecting decision making
o Technology/Information/Computers
o Structural Complexity/Competition
o International Markets/Political Stability
o Consumerism/Changes, Fluctuations
Information, Decisions, and Management
 Information
o Type of information required is directly related to
the level of management and the amount of
structure in the decision situation
 Levels
of managerial decision-making
o Strategic Management
o Tactical Management
o Operational Management
Strategic Management

Monitor the strategic performance of the organization and its
overall direction in the political, economic, and competitive
business environment
 Unstructured
Decisions
o Not possible to specify in advance most of the decision
procedures to follow
o Decision maker must provide judgement, evaluation and
insights to a novel, important and nonroutine-type decision

Require more summarized, ad hoc, unscheduled reports,
forecasts, and external intelligence to support their more
unstructured planning and policy-making responsibilities
Tactical Management

Allocate resources and monitor the performance of their
organizational subunits, including departments, divisions,
process teams, and other workgroups
 Semistructured
Decisions
o Some decision procedures can be prespecified, but not
enough to lead to a definite recommended decision
o Only part of the decision has a clear-cut answer provided
by an accepted procedure

Require information from both the operational level and the
strategic level to support their semistructured decision making
responsibilities
Operational Management

Direct the use of resources and the performance of tasks
according to procedures and established budgets and
schedules
 Structured
Decisions
o The procedures to follow when a decision is needed can be
specified in advance
o Involves a repetitive and routine-type decision where there
is a definite procedure to follow

Require more prespecified internal reports emphasizing
detailed current and historical data comparisons that support
day-to-day operations
Decision Support Technologies
 Management
Information Systems (MIS)
 Decision Support Systems (DSS)
 Enterprise (Executive) Information Systems (EIS)
 Enterprise Resource Planning (ERP) and SupplyChain Management (SCM)
 Knowledge Management Systems
 Expert Systems (ES)
 Artificial Neural Networks (ANN)
OLAP
 Online Analytical
Processing (OLAP)
o A capability of management, decision support,
and executive information systems that enables
managers and analysts to interactively examine
and manipulate large amounts of detailed and
consolidated data from many perspectives
 Basic
analytical operations include
o Consolidation: aggregation of data
o Drill-Down: display detail data
o Slicing & Dicing: produce different views from
database
Decision Support Systems


Computer-based information systems that provide
interactive information support to managers during the
decision-making process
DSS use:
o
o
o
o
Analytical models
Specialized databases
Decision makers’ own insights and judgements
Interactive, computer-based modeling processes to support the
making of semistructured and unstructured decisions by individual
managers
o Data mining analysis of large pools of data to find patterns and
rules that can be used to guide decision making and predict future
behavior
Decision Support Systems

Using a DSS involves four basic types of modeling activities:
o What-if Analysisan end user makes changes to variables, or
relationships among variables, and observes the resulting changes
in the value of other variables
o Sensitivitiy Analysisa special case of what-if analysis—the value
of only one variable is changed repeatedly, and the resulting
changes on other variables are observed.
o Goal Seeking Analysissets a target value for a variable and then
repeatedly changes other variables until the target value is achieved
o Optimization Analysisthe goal is to find the optimum value for
one or more target variables, given certain constraints
DSS Applications
According to a recent survey, computer-based DSS are
widely applied in both profit making and non-profit
organizations. In corporate functional management fields,
production and operations management contain the largest
number of application articles, followed by management
information systems, marketing, finance, strategic
management and multifunctional areas. The following
website list some of the important application examples from
the survey.
http://cstl-hcb.semo.edu/eom/ORINSIHT.HTM
Management Science
A field of study that uses computers, statistics, and mathematics
to analyze and solve business problems
The Problem Solving Process
Identify
Problem
Formulate &
Implement
Model
Analyze
Model
Test
Results
Implement
Solution
unsatisfactory
results
Computer Model: A set of mathematical relationships and
logical assumptions implemented in a computer as an abstract
representation of a real-world object or phenomenon
A Generic Mathematical Model
Y = f(X1, X2, …, Xk)
Where:
Y = dependent variable (a bottom line performance measure)
Xi = independent variables (inputs having an impact on Y)
f(.) = function defining the relationship between the Xi and Y
Categories of Mathematical Models
Model
Category
Prescriptive
Predictive
Descriptive
Form of f(.)
Independent
Variables
OR/MS
Techniques
known,
well-defined
known or under
decision maker’s
control
LP, Networks, IP,
CPM, EOQ, NLP,
GP, MOLP
unknown,
ill-defined
known or under
decision maker’s
control
Regression Analysis,
Time Series Analysis,
Discriminant Analysis
known,
well-defined
unknown or
uncertain
Simulation, PERT,
Queueing,
Inventory Models