Lecture No. 11
CSE1720 Semester 1 2005 week 11 / 1
Lecture Objectives
1. To provide you with some contact with Decision Making
Processes and to illustrate support from Computer
Technology
2. A brief run through those facilities which are generally
classified as Office Automation
3. To look at some of the aspects which need to be addressed
in the selection of Hardware and Software
CSE1720 Semester 1 2005 week 11 / 2
SOME ASPECTS OF
THE DECISION PROCESS
CSE1720 Semester 1 2005 week 11 / 3
Goedels’ Theorem
“Mathematical statements exist for which no systematic
procedure could determine whether they are true or false”
also known as undecidable propositions
Some statements :
‘This statement is a lie’
‘We cannot prove this statement to be true’
Socrates : ‘ What Plato is about to say is false’
Plato
: ‘ Socrates has spoken truly’
If the statement is true then it is false
If it is false, it is true. [self referential paradoxes]
CSE1720 Semester 1 2005 week 11 / 4
The Decision Process
INTELLIGENCE
Determine Conditions
Requiring Management
Attention/Decision
DESIGN
Develop and Analyse
Possible Courses
(Alternatives) of Action
CHOICE
Select a particular course
of action from the available
alternatives (models, QA,
Projections)
CSE1720 Semester 1 2005 week 11 / 5
Decision Making
• Rules form an important part of the decision-making
environment of an organisation (enterprise)
• Rules may be
– word of mouth
– referenced in a rules manual
– embedded in application code (DBMS Integrity)
– installed in a separate structure (e.g. law)
Rules affect
– hiring and firing procedures
– product return policies
– sales markdown strategies (January sales ?)
– manufacturing methods
CSE1720 Semester 1 2005 week 11 / 6
Decision Making
•
•
•
•
Can there be decisions without rules ?
What conditions, agendas, goals can affect a decision ?
Can the ‘reasons’ for decisions be analysed ?
Is there some way of knowing that the ‘right’ decision was
made ?
• Decisions are frequently associated with ‘action’
• Decisions may be about ? ? ?
– Goals of a corporation (enterprise) - for instance
diversification or concentration
– Rules of a corporation - e.g. dress code on Fridays to be
casual (Telstra)
CSE1720 Semester 1 2005 week 11 / 7
Decision Making
• Another example is a decision to alter a predictive model.
Business and Financial Analysts may change the
components or domains for credit risk prediction - any
recent examples spring to mind ?
• Decisions can only be implemented on things which can be
changed
– Is a ‘decision’ to increase sales by say selling solar
panels on Jupiter or Mars really a decision ? Can it be
implemented ?
CSE1720 Semester 1 2005 week 11 / 8
Decision Making
• Making a decision is the function of combining goals and
predictive models
– The lowering of prices of some products (e.g. K-Mart
sales) is the result of
• a goal to maximise sales
• a model which relates sales to prices
– The denial of credit by a bank to a loan applicant is the
result of
• a goal to minimise loan write-offs
• a predictive model which relates selected applicant
attributes (properties) with the likelihood of a loan
default
CSE1720 Semester 1 2005 week 11 / 9
Decision Making
• Without goals there would be difficulty in deciding what
course of action to take.
• Without the goal of maximising sales, there is no correct
decision concerning product pricing
• Without a predictive model which equates product prices to
product sales, there is no clear indication which decision will
be most likely to maximise sales
CSE1720 Semester 1 2005 week 11 / 10
Decision Making
Consider these decision making ‘challenges’
1. The need to automate some decision-making functions
2. The need to ensure consistent decisions
3. Difficulties in analysing how a decision was made
4. Complexities in the predictive model
5. Difficulties in interpreting stated goals (which may change)
6. Instability in the goals
7. Interpersonal dynamics (know any recent examples ?)
8. Fluctuations in the predictive models
9. Conflict between data-driven and model-driven
understanding or ‘knowledge (beliefs)’
CSE1720 Semester 1 2005 week 11 / 11
Decision Making
Business-rule automation tools focus on
1. The need to automate some decision-making functions
2. The need to ensure consistent decisions
Decision analysis tools focus on
3. Difficulties in analysing how a decision was made
4. Complexities in the predictive model
5. Difficulties in interpreting stated goals (which may change)
6. Instability in the goals
Group decision-support tools focus on
7. Interpersonal dynamics
CSE1720 Semester 1 2005 week 11 / 12
Decision Making
And items 8 and 9 ?
8. Fluctuations in the predictive models
9. Conflict between data-driven and model-driven
understanding or ‘knowledge (beliefs)’
more on these later on.
Business rules connect to transaction systems and help to
automate decision-making processes which were previously
the function and responsibility of persons - the goals are
fixed and are explicit.
CSE1720 Semester 1 2005 week 11 / 13
Decision Making
Decision-analysis tools (software)
Decisions are based on multiple predictive models
There are complex measures of uncertainty or imprecision
The goals may be variable
Decision analysis is related to operations research - the area
where
– mutually exclusive goals
– shared scarce resources
The intention is to maximise profit, stability
CSE1720 Semester 1 2005 week 11 / 14
Decision Making
Group decision support tools
Consider the situation of many managers of an organisation
attempting to arrive at a common decision to
– make 300 staff redundant
– increase sales to justify no redundancies
– increase sales and increase the number of staff
– reduce staff but maintain existing sales or improve sales
Interpersonal / political challenges
Anonymous electronic meeting environment
Vote on merit of ideas rather than on identities
CSE1720 Semester 1 2005 week 11 / 15
The Decision Process
Stage
1.
2.
3.
4.
5.
6.
7.
8.
Description
Determine objectives, problems
Identify courses of action available to
achieve / rectify
Collect Information to assess available
options
Select criteria for evaluation purposes
Evaluate information acquired
Select preferred course of action / strategy
Implement chosen option / strategy
Monitor results - post analysis
CSE1720 Semester 1 2005 week 11 / 16
Decision Support Systems
Characteristics:
Interactive Computer Base Information Systems
Decision Models - Statistical Forecasting, Profiling ...
Management Data Base
OUTPUTS:
Information ‘tailored’ to SUPPORT specific
decisions faced by Managers ( Car Industry,
Manufacturing Industry, Farming Industry,
Financial, Accounting etc ...)
CSE1720 Semester 1 2005 week 11 / 17
Decision Support Systems Components
Data Base
Report Writer
Graphics
Computing Facilities - Processor, Storage, I/O Devices
Communications
Human Skills:
Objectivity
Clear Thinking
Lateral Thinking
Adaptability
Communication
Analytical Ability
Computer Literacy
Tenacity
CSE1720 Semester 1 2005 week 11 / 18
The Decision Makers
Who are ‘The Decision Makers’ ?
In the early days of decision support, the Decision Makers
were a small group of high-level executives (does this
sound familiar ?)
Since then however, the business intelligence industry has
helped push data-driven decisions to a much wider user
environment
CSE1720 Semester 1 2005 week 11 / 19
The Decision Makers
Today, the ‘decision makers’ are business people who are
closest to the point where an action needs to be taken.
This can be:
– in the supply chain
– when in contact with a customer (email, web-mail,
telephone, (fax ?)
– at a strategic executive meeting
CSE1720 Semester 1 2005 week 11 / 20
Business Intelligence
Business Intelligence addresses :
Synthesising or constructing useful knowledge from large
sets of data
It involves
integration
summarisation
abstractions
ratios
trends
allocations
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Business Intelligence
It addresses
comparing generalisations based on data with modelbased assumptions
reconciling these when they differ
creative thinking supported by data
using data carefully
understanding how to calculate derived data
continual learning
modifying goals
CSE1720 Semester 1 2005 week 11 / 22
Business Intelligence
The functions which support Business Intelligence are
– data collection
– data storage (why ?)
– data translations - time, currencies
– dimensional structuring (allows for extractions on a
number of bases)
– access models
– predictive models
– model verification
– knowledge sharing
– resource allocation scenarios
– decision implementation strategies
CSE1720 Semester 1 2005 week 11 / 23
Decision Support Systems
Provide a quick response to SIMULATED problems (software
support)
Generally LESS COSTLY than real life exercises
Variety of ‘business decision models’
- linear programming
- decision trees
- simulation
- queueing
- financial analysis DCF, NORMDIST, NPV
- forecasting / projections
Which one(s)
- risk analysis
best suit the
- sensitivity analysis
conditions ?
CSE1720 Semester 1 2005 week 11 / 24
Decision Support Systems Software
• Model Building
– Relationships between parameters
• What-if Incremental Assumptions
– Highly useful aspects
• Backward Iteration
– Establish a Target and work back - ( ? regression)
• Risk Analysis
– Use probability distributions to assess outcomes
• Statistical Analysis and Management Science Models
– Regression
Time Series Analyses
• Financial Functions
– Depreciation Methods
Return on Investment
CSE1720 Semester 1 2005 week 11 / 25
Decision Support Systems
• Programmable Tasks = Rules / Procedures Known
* Clear Rules
* Rules can be built into a software program
* All required data is available
* The Decision maker is supported by software
processes
* Complex situations may indicate a very deep but
‘modular’ and / or progressive structure
• Some Examples:
* Mergers, Takeovers, Off-Loadings
* Plant Expansion
* New Products
* Portfolio Management * MarketingCSE1720 Semester 1 2005 week 11 / 26
Decision Processing
Decision Processing
Extract/transform/load
(ETL) tools
Information templates
ERP and other
transaction-processing
systems
Oracle, SAP, People Soft
Business Intelligence (BI) tools
Reporting/analysis templates
Packaged analytic applications
Federated
data warehouse
Web based
Information Portal
SAP’s Business Information
Warehouse
Information flow in a decision-processing system
CSE1720 Semester 1 2005 week 11 / 27
Application Taxonomy
Application Segment
Financial Management
Customer Relationship
Management (sales,
marketing, service)
Application Types
Financial consolidation
Budgetting and planning
Cost and profitability analyses
Risk management
Fraud detection
Customer profitability, retention,
cross and up selling
Customer segmentation,
behaviour analysis
Sales force analysis
Promotion and campaign analysis
Product performance analysis
CSE1720 Semester 1 2005 week 11 / 28
Application Taxonomy
Application Segment
Supply chain performance
management
Human resource Management
Executive Business
performance management
Application Types
Demand planning
Inventory control
Distribution efficiency and
optimisation
Workforce planning and
optimisation
Salary planning and analysis
Employee retention
Scorecard/key performance
indicators
CSE1720 Semester 1 2005 week 11 / 29
Application Taxonomy
E- business management
Taxonomy means classification
Promotion analysis and
channel comparison
Inventory control and
supplier analysis
Product and shopping
analysis
Support from packaged decision-processing solutions:
SAP : Business Information Warehouse - SAP R/3 transaction
processing
Oracle : Sales Analyser. Financial Analyser. Activity Based
Costing. Balanced Scorecard.
Others : IBM, Information Builders, Informatica, Sybase, ...
CSE1720 Semester 1 2005 week 11 / 30
Decision Support Systems
• Non-Programmable Tasks
* Unstructured = No Definable Rules
* Does not permit software programs to be developed
* Cannot determine :
- Objectives
- Trade Offs
- Relevant Information
- Methods for analyses
CSE1720 Semester 1 2005 week 11 / 31
Decision Support
• Some Offsets:
Managers tend to be busy and highly paid
This will normally lead to a reluctance to learn the
‘special features’ of a software package
OR to understand the problem which the software BEST
addresses
• A brief and cursory understanding may lead to
– lack of understanding of limitations
– lack of clarity in interpretation of results
CSE1720 Semester 1 2005 week 11 / 32
Decision Support
• Related Matters
Economic models invariably are developed for
‘General Cases’
Quality of Information Used
Some models have default values/options - may not be
suitable for specific instances
CSE1720 Semester 1 2005 week 11 / 33
Decision Support
Uncertainties - types and sources of
- effects on decision making
A few examples:
– response to direct mailings
– Internet home page accesses
– default rates for loans
– sales reports
• sales reports - doubts - are ALL sales reflected ?
- how is ‘missing’ data handled - 0 ?
- is the program 100% error free ?
• Can such doubts be quantified ? Should they be ?
CSE1720 Semester 1 2005 week 11 / 34
Business Intelligence
Data uncertainty can be : predictions
historical
Budgeting, marketing are widely analysed using
spreadsheets.
Uncertaintities are handled (generally) with a single valued
estimate.
Next year’s sales may include a single estimate in the
budgeting exercise.
Healthcare (as in Medicare) may be based on a single value
for doctors’ productivity (or hospital case-mix).
CSE1720 Semester 1 2005 week 11 / 35
Business Intelligence
Let’s look at a company which is trying to float a new
product, or increase its sales of an existing product.
5 possible promotional methods are available
– radio
– newspaper (local, local/country, local/interstate ?)
– television advertising
– direct mail
– an ‘all-bells and whistles’ presence on the World Wide
Web
There is a hidden agenda - what is the Competition doing
or how is it going to react ?
CSE1720 Semester 1 2005 week 11 / 36
Business Intelligence
There could be :
– no competition
– low competition
– medium competition
– high competition
– multiple competitor competition (e.g. car industry)
– and what is ‘low’, ‘medium’, ‘high’ ? How are the
parameters set ?
• A decision analysis tool will accommodate a probabilistic
component.
• The ‘unknown’ is a spreadsheet model is the range of
likelihood of competitive promotions, and of course their
effect on sales
CSE1720 Semester 1 2005 week 11 / 37
Business Intelligence
A decision analysis tool will simulate a number of scenarios
based on the specified probabilities, and will indicate the
decision which will (in this case) have the best likelihood of
maximising profit.
And the past ? - meaning legacy or historical data ?
– Quality of data is important here
– Customer code structures - any changes over 3 to 5
years
– Customer name spelling ?
– Incorrect replication
– Regional boundary alterations ? - are we able to
compare oranges to oranges ?
CSE1720 Semester 1 2005 week 11 / 38
Business Intelligence
• What about missing data - is it shown as zero ?
• Data in the wrong field - a name in an address field ?
• The number of items on an invoice = the number actually
received ?
• Deliberate errors on response cards - age, income, number
of people living at an address, types of goods normally
purchased etc.
• And finally, does software assume for example an even
distribution of error ?
CSE1720 Semester 1 2005 week 11 / 39
Office Automation
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Office Automation
Demands
Possible Solutions
Changes in Technology
Data Planning
Competitive Advantage
4GL / Windows XX
Business Justification
Planning Methods
Organisational Changes
Mainframe/Mini/PC
Information Systems
Office Automation
Decision Support
VideoText
Project Evaluation
Database Technology
CSE1720 Semester 1 2005 week 11 / 41
Office Automation
• Some Current ‘New’ Software:
Crystal INFO - Workgroup Decision Support for 5 Users
Interbase - The scalable SQL Server
Power Builder ‘For delivering fast applications anywhere’
Centura - Client Server Applications
CASE/Modelling Tools 44 products
Client/Server Application Testing 21 products
Data replication 9 products
Database Accounting 23 products
Database Application Development 88 products
Decision Support, OLAP 52 products
CSE1720 Semester 1 2005 week 11 / 42
Office Automation
Also known as Personal Productivity Tools
Some Examples
Spreadsheets
Word Processing / Desktop Publishing
Electronic Mail
Tele Conferencing
Electronic Funds Transfers
Electronic Document Filing (and retrieval)
Electronic Document Interchange *
Windows
User Networks
Textual Databases
and: Smart Telephones, Fax, Videotext, Electronic Bulletin
Boards
CSE1720 Semester 1 2005 week 11 / 43
Electronic Data Interchange
This is the process of electronically transferring data in
document which have been specially formatted
The documents are moved between businesses, and include
documents such as orders, invoices, contracts, receipts,
transfers, credit approvals, delivery and shipping
documents.
They are normally formatted or structured documents,
whereas emails are unstructured or free-form
Normally called ‘EDI’
CSE1720 Semester 1 2005 week 11 / 44
Electronic Data Interchange
Typical examples are :
– Ordering of Goods and Services ( ? Internet)
– Delivery Co-Ordination
– Negotiation of Prices
– e.g. Computer Aided Livestock Marketing
– Stock Exchange
The documents are repetitive - meaning that they occur
frequently in the business operations
There is an ANSI standard for Canada and the USA.
CSE1720 Semester 1 2005 week 11 / 45
Electronic Document Interchange
• Banking, Insurance, Superannuation,Financial Services,
Production, Retail, Transport.
• Data Security : Adoption by Australia of the OECD
Cryptography Policy Guidelines
Implementation phase of the
SWIFT/BOLERO model for electronic
negotiable bill of lading
Introduction of Australian legislation tp
provide powers to regulate electronic
documents for sea carriage of goods
CSE1720 Semester 1 2005 week 11 / 46
EDI Electronic Document Interchange
– Acceptance of all major international bankcard
operators of their agreement to the use of the ‘SET’
(Secure Electronic Transaction) protocol for credit
card payments and collections
– The successful use of Tenet, the first intranet based
fully electronic court room in the Victorian Supreme
Court (a full trial without there being any paper
documents presented in the court)
• However, there are delays in reforms and draft legislation to
enact the privacy principles of the European Union.
• And, there are delays in some States with laws of evidence
in the Evidence Act 1995.
CSE1720 Semester 1 2005 week 11 / 47
EDI Electronic Document Interchange
Management Advantages
– Reduction / Elimination of Data Entry Staff
– Security (improved with the use of the Internet)
– Speed
– Accuracy
– Confirmation of Process
CSE1720 Semester 1 2005 week 11 / 48
Hardware and Software Selection
CSE1720 Semester 1 2005 week 11 / 49
Hardware/Software Selection
CSE1720 Semester 1 2005 week 11 / 50
Hardware and Software Selection
• Some Current ‘New’ Software:
Crystal INFO - Workgroup Decision Support for 5 Users
Interbase - The scalable SQL Server
Power Builder ‘For delivering fast applications anywhere’
Centura - Client Server Applications
CASE/Modelling Tools 44 products
Client/Server Application Testing 21 products
Data replication 9 products
Database Accounting 23 products
Database Application Development 88 products
Decision Support, OLAP 52 products
CSE1720 Semester 1 2005 week 11 / 51
Hardware and Software Selection
• Some Common Management Concerns:
– Will it perform to ‘Expectations’
– Will it provide the ‘required management support’
– Will it be a good investment
– Does the organisation (person) have
– a real need
– the appropriate environment
Prepare: Analyse, Plan, Review, Decide, Communicate
and obtain other user’s experience
CSE1720 Semester 1 2005 week 11 / 52
Hardware and Software Selection
System Design Stage
Develop CRITICAL ITEMS - Hardware Software Budget,
Delivery Time, Turnkey,
Performance, Uptime
ESSENTIAL ITEMS - Expansibility, Compatibility
- Software, Clear Statement of
deliverables, Supply of manuals,
training, vendor record
- Quantification of Hardware, Installation
Assistance, Warranty
NORMAL ITEMS
OTHER
CSE1720 Semester 1 2005 week 11 / 53
Hardware and Software Selection
It’s also worthwhile to do some Reliability Sampling
analyses on the Permutations to develop a comfort index.
SPSS ?
CSE1720 Semester 1 2005 week 11 / 54
Hardware and Software
Selection
Areas or Aspects of Submissions for Assessment
Hardware:
Expansion capability
Reliability
Maintenance
Software
Operating System and Upgrades
Communications Capability
Training
Security
CSE1720 Semester 1 2005 week 11 / 55