Download Lecture 4 - Enhancing Management Decision Making Part 1

Managing Information
Systems
Enhancing Management Decision Making
Part 1
Dr. Stephania Loizidou Himona
ACSC345
Objectives
 To understand types of decision-support
systems
 To understand the components of a
decision-support system
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Decision-support Systems
 What is a decision-support system (DSS)?
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MIS or DSS?
 Management Information Systems:
– Routine reports (periodic)
– Assist control of an organisation
 Decision-support Systems:
– Non-routine
– Support flexibility and rapid response
– Semi-structured or unstructured data
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Types of DSS
 Model-driven
– Uses a model to perform ‘what if’ analysis
– Typically standalone
– In-house or departmental
– Strong theory or model
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Types of DSS
 Data-driven
– Analyse large amounts of data
– Data from TPS into data warehouses
– Use
 On-line Analytical Processing (OLAP)
 Data mining
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Data-driven Examples
 Contrast
– How many widgets were shipped in December?
 With
– Compare the sales of widgets to the sales plan
by quarter and sales region for the last two
years?
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DSS Components
TPS
User
Interface
DSS
Database
External
Data
DSS Software System:
Models
OLAP Tools
Data Mining Tools
User
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DSS Models
 Abstract representation that illustrates the
components or relationships of the problem
– Physical: model of an airplane
– Mathematical: profit = revenue - costs
– Verbal: description of a procedure
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DSS Models




Statistical (typical)
Optimisation
Forecasting
Sensitivity analysis
– “What if”
– Repeatedly modify parameters of model to
determine outcome
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OLAP
(On-line Analytical Processing)
 Dynamic multi-dimensional analysis of
enterprise data
 Just-in-time information
 Wide variety of views of information
 Transformation of raw data:
– Reflects the ‘real’ dimensionality of enterprise
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OLAP
 Data:
– Loading – bulk and operational, internal and external
– Aggregation
 Processing:
– Application of business models and statistics
 Querying:
– Complex
– Drill-down through hierarchies
– Ad-hoc
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Data Mining
 Provides a way of finding hidden insight not
obtained by traditional techniques.
 Uses:
– Statistical analysis
– Neural networks
– Fuzzy logic
– Genetic Algorithms
– Rule-based systems
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Data Mining
 Associations
– Occurrences linked to a single event
 Example
– Supermarket purchases
– When crisps are bought, 85% of the time a can
of Coca-cola is bought
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Data Mining
 Sequences
– Events linked over time
 Example
– House purchase
– Within two weeks, 65% of the time a refrigerator
is bought
– Within one month, 45% of the time an oven is
bought
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Data Mining
 Classification
– Recognise pre-defined patterns to group similar
items
 Example
– Telephone operators
– Recognise those attributes of customers who
are likely to leave
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Data Mining
 Clustering
– Recognise patterns to cluster similar items
without pre-defined groups
 Example
– Bank customer details
– Partitioning data into groups by demographics
or investments
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Data Mining
 Forecasting
– Use existing data to forecast future values
 Example
– Past performance to predict sales figures
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DSS Examples
 Supply Chain Management
– Who, what, when and where?
– Purchasing, manufacture and distribution
 Customer Relationship Management
– Pricing
– Customer retention
– New revenue streams
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DSS Examples
 Business Scenarios
– Sensitivity analysis of business parameters
– Cost / benefit analysis
 Geographic Information Systems (GIS)
– Display information geographically
– Demographics, customers, crime
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Example
Questions
1.
2.
3.
4.
Who are our most
frequent customers?
Do they live close to
our shops?
How can we resegment those
customers?
How can we better
reach those
segments?
1.
Customer data warehouse
•Legacy data
•Website transactions
•Call centre data
•External data
2.
3.
4.
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Analysis
Use statistical
analysis to find top
25% most frequent
customers.
Establish correlation
between location
and sales
Verify new
customer segments
Query database on
customer
information per
segment
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