Time/place Framework pg 441

The effectiveness of collaborative computing technology depends on the location of the group members and on the time that shared info is sent and recieved.

When information is sent and recieved almost simultaneously, the communication is said to be synchronous (real-time): Telephones,IM and face to face meetings are egs.

Asynchronous communication occurs when the reciever gets the information at a different time than it was sent: email

Groupware

primary objective to support group work.

can support direct or indirect decision making

pro's/cons for working in groups pg 437

Group decision making process pg 436

characteristsics of groupwork page 436

• performs a task
• may be located in different locations
• may work at different times
• work for the same of different orgs
• groups is remporary or permanent
• can span several managerial levels
• may be synergy or conflict
• creates gains/or losses in productivity
• may have t be done quickly
• may be impossible or too expensive for all team members to meet in one location face to face
• needed data may come from many different sources, including external ones
• expertise of non team members maybe required
• should concerntrate on decision making

Electronic Meeting Systems (EMS)

Electronic Meeting Systems (EMS) is a collaborative tool for GSS

Different Time

General characteristics include:

• If one member disagrees, the others may well need re-consulting (chasing one’s tail situation)
• By keeping each team member’s comments confidential, members tend to not feel constrained or threatened (junior members often feel less inhibited as a result)

Same Time

General characteristics include:

• Preferable for decision-making
• Facilitated by:

1. video-conferencing, and
2. multi-media presentations
3. electronic white boards,

• Data/document sharing comparatively simple
• Web based GSS preferable support tool

Collaboration support

Four scenarios:

• Same place, same time
• Same place, different time
• Different place, same time
• Different place, different time

communication Support

• Vital element for decision support
• Modern information technologies via the Web provide:

1. Inexpensive
2. Fast
3. Capable
4. Reliable

means of supporting communications

• Enabling Platforms:

1. Internet/Intranet/Extranet

Group Support Systems

• Group Decision-Making
• Group Communication

1. Communication support

• Group Collaboration

1. Collaboration support

• Group Support Systems

1. Technologies/Tools

Internal Data page 98

The term “internal data” is used here to denote any information useful to the decision-making process found within the company.

http://www.gestiondesarts.com/index.php?id=783

Levels of DSS models - Strategic, Tactical, Operational page 105

http://www.smallbusinesshq.com.au/factsheet/20305-Tips-on-Strategic-Tactical-and-Operational-Decision-Making.htm

Strategic Decisions - 'What?'

Strategic decisions deal with the big picture of your business. The focus of strategic decisions is typically external to the business and usually future oriented. Strategic decision-making creates the forward thrust in the business.

It includes decisions about:

• What business are you in?
• What is your vision for the business?
• What's your business' identity?
• What do you stand for?
• Which direction is the business headed?
• How will the business compete?

Corporations often capture their overall business strategy in a "Statement of Intent" and it's an excellent term for describing what strategic decision-making is. Too often people confuse strategic decisions with tactical decisions and fail to really examine the big picture. It can lead to stagnation in the business and an inability to move forward.

Top managers

Tactical Decisions - 'How?'

Tactical decisions involve the establishment of key initiatives to achieve the overall strategy. For example, if you have decided to be the Number 1 provider in your market (a strategic decision) then you will develop tactics (e.g. implement a marketing system, increase number of therapists) to achieve that outcome. In a small business you may have 4 or 5 key tactics that you are going to use to achieve your overall strategy.

Again this layer of decision-making can sometimes be overlooked yet it is the glue that creates a strong connection between your long-term vision and your day-to-day activities. Tactical decision-making is the domain of 'mission' statements.

Think in terms of the battlefields from which the term has emerged. The overall strategy, that is, what the army is there to do, is to win the war. Then you have a number of 'missions' you send troops on, preferably diplomatic ones, the cumulative effect of which is intended to win the war.

Operational Decisions - 'How will we deploy resources?'

Operational decisions determine how activities actually get done. They are the 'grass roots' decisions about who is going to do what and when. It includes:

• How will we spend our money this month?
• How will we service that client?
• What is our procedure for delivering an order?
• Who will be doing quality control?

If you are making decisions involving processes and procedures they are usually operational decisions. Operational decisions are often made in 'real time' and are the result of needing to make quick adjustments or change to achieve the desired outcome.

http://www.enotes.com/business-finance-encyclopedia/decision-making

STRATEGIC, TACTICAL, AND OPERATIONAL DECISIONS

People at different levels in a company have different types of decision-making responsibilities.

Strategic decisions, which affect the long-term direction of the entire company, are typically made by top managers. Examples of strategic decisions might be to focus efforts on a newproduct or to increase production output. These types of decisions are often complex and the outcomes uncertain, because available information is often limited. Managers at this level must often depend on past experiences and their instincts when making strategic decisions.

Tactical decisions, which focus on more intermediate-term issues, are typically made by middle managers. The purpose of decisions made at this level is to help move the company closer to reaching the strategic goal. Examples of tactical decisions might be to pick an advertising agency to promote a newproduct or to provide an incentive plan to employees to encourage increased production.

Operational decisions focus on day-to-day activities within the company and are typically made by lower-level managers. Decisions made at this level help to ensure that daily activities proceed smoothly and therefore help to move the company toward reaching the strategic goal. Examples of operational decisions include scheduling employees, handling employee conflicts, and purchasing rawmaterials needed for production.

It should be noted that in many "flatter" organizations, where the middle management level has been eliminated, both tactical and operational decisions are made by lower-level management and/or teams of employees.

Regression analysis

regression analysis refers to techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.

Regression analysis is widely used for prediction (including forecasting of time-series data).

from wiki: http://en.wikipedia.org/wiki/Regression_analysis

Semi structured problems page 14

Fall between structured and unstructured problems, having some structured elements and some unstructured elements.

eg's

• Trading bonds
• setting marketing budgets
• capital aquisition analysis

see chart

Modelling and Analysis

Sean O'Sullivan: Modelling - building a model of the problem that will generate the alternatives. Sep 29, 2009 1:00:53 PM EST
Sean O'Sullivan: Analysis - incorporating the functions and process that will evalueate each alternative. Sep 29, 2009 1:01:55 PM EST
Sean O'Sullivan: 'processes that will evaluate' Sep 29, 2009 1:02:16 PM EST
Sean O'Sullivan: And then we strike a BIG problem! Sep 29, 2009 1:02:58 PM EST
Sean O'Sullivan: which is -- many problems are very different from each other. for example ' what is the best roster for next month?' or 'how shoudl I spend my marketing budget for the year?' Sep 29, 2009 1:04:23 PM EST
Sean O'Sullivan: Each of these two problems require different modelling and analysis, and each will probably need a different DSS. Sep 29, 2009 1:05:23 PM EST

Can you give an example of how a DSS might help wiht intelligence?

Sean O'Sullivan: A DSS that is focused on a database might provide a list of all the staff who are available at the required times with associated lists of their skills and costs (wages etc)... Sep 29, 2009 12:42:52 PM EST
Sean O'Sullivan: And you as the manager can then use this information to develop several alternative rosters ...(Design) Sep 29, 2009 12:43:39 PM EST
Sean O'Sullivan: .. and then choose the best roster ... (Choice) ... and then put it into place (Implementation) Sep 29, 2009 12:45:39 PM EST
Sean O'Sullivan: It can be useful to think of decision support as a COLLABORATION between a human decision-maker and a DSS. Sep 29, 2009 12:49:00 PM EST

Sean O'Sullivan: It turns out that the simplest of teh four stages to do aas a DSS is teh design stage. Iterlligence is very complex, choice is untrustworthy, and implementation is often outside teh scope of automated systems. ( Iw ant the house painted pink!) Sep 29, 2009 12:54:00 PM EST

What is the relationship of the DSS and its components to a MSS?

JENNIFER MCCOWATT: I figure a DSS is a variety of MSS with special features Sep 29, 2009 1:25:22 PM EST
Sean O'Sullivan: MSS = Management support system; DSS = Decision support system; Managers do many things including making decisions. A DSS is a specialised form of MSS that focuses on helping managers make good decisions. Sep 29, 2009 1:27:11 PM EST

What is a 'static' model?

Sean O'Sullivan: You might use a static model a vast number of times. Sep 29, 2009 1:10:53 PM EST
Sean O'Sullivan: If you have a still camera and a movie camera, which will produce a static model? Sep 29, 2009 1:12:19 PM EST
JENNIFER MCCOWATT: still camera Sep 29, 2009 1:12:27 PM EST
Sean O'Sullivan: Corect, but why do you think this is so? Sep 29, 2009 1:12:49 PM EST
Sean O'Sullivan: corect = correct Sep 29, 2009 1:12:58 PM EST
JENNIFER MCCOWATT: Because it is one shot. Many shots would produce dynamic Sep 29, 2009 1:13:23 PM EST
Sean O'Sullivan: yes a static model represents a single 'snapshot' of the problem with a sepfied set of inputs and conditions. 'What will the monthly payments on my home loan be if I borrow $100,000 for 20 years at a fixed interest rate of 4%?' Sep 29, 2009 1:15:47 PM EST
Sean O'Sullivan: sepfied = specified Sep 29, 2009 1:16:09 PM EST
Sean O'Sullivan: And I could use the same static model again but with a different set of inputs and conditions ..".What will the monthly payments on my home loan be if I borrow $200,000 for 15 years at a fixed interest rate of 3.5%? Sep 29, 2009 1:17:25 PM EST
If you have a still camera and a movie camera, which will produce a dynamic model? Sep 29, 2009 1:17:51 PM EST
JENNIFER MCCOWATT: the movie camera Sep 29, 2009 1:17:59 PM EST
Sean O'Sullivan: Yes -- "Many shots would produce dynamic" -- Dynamic models are usually (but not always) associated with time. Sep 29, 2009 1:19:38 PM EST
JENNIFER MCCOWATT: Right Sep 29, 2009 1:20:34 PM EST
Sean O'Sullivan: Particulary if the problem involves inputs and conditions that do or might change with time. Sep 29, 2009 1:20:42 PM EST
JENNIFER MCCOWATT: Like a variable interest rate? Sep 29, 2009 1:21:07 PM EST
Sean O'Sullivan: Good example, and the dynamic model for such a problem would have to include some way to model the variation in the interest rate. Sep 29, 2009 1:22:25 PM EST

ass 2 links

http://dssresources.com/

http://dssresources.com/faq/index.php?action=artikel&id=56

http://www.bestpricecomputers.co.uk/glossary/decision-support-systems.htm

The components of DSS Mathematical problems PART 2 page 92

Data management subsystem

• inlcudes a database that contains relevenat data, managed by DBMS. CAn be interconnected with DW.

1. DSS database

• internal data: from orgs Transaction Processing System (TPS). Monthly payroll, operational data from functional areas of the business, machine maintenance scheduling. future hiring pans. Included in DW.
• external data: industry data, market research consensus data, govt regs, tax rate schedules, national economic data.
• private data: guidlines used by specific decision makers as assesments of specific data and or situations.
  

1. DBMS
2. Data query
3. Query facility
   

Model management subsytem

software package: financial, statictsical, management science, quantitive models that provide analytical capabilities and software mgt. Often call model base management system MBMS. Can be connected to external storage.

• Model base

1. Strategic models: used by top managers for strategic planning responsibilities.
2. Tactical models: used by middel managers for allocation and controlling orgs resources.
3. Operational models: support day to day working activities.
4. Analytical models: used to perform analysis on data.
   

• MBMS
• Modeling langugae
• Model directory
• Model execution, integration, command processor.
  

User interface subsytem

User commiunicated with and comand the DSS. User is considered part of the system.

Knowledge based management subsytem

can support any of the other components or act as independant component. Augments system/ decision maker with intelligence.

Differenc between DSS and BI page 90

DSS generaly built to solve a specific problem and contains its on databases.

BI apps focus on reporting and identify problems by scanning data extracted from the DW (Date Warehouse)

DSS Characteristics and capabilities page 90

Total Cost of Ownership TOC

Return on Investment ROI

Simulation types page 167

Probalistic Simulation: One or more of the dependant variables are probalistic

• Discrete distributions: limited number of events or variables, finite number of values
• Continuous distributions: unlimited number of possible events

Time Dependant Vs Time Independant Simulation:
Time Independant refers to a situation where it is not important to know exactly when the event occured
eg. Important to know that 3 units were sold in 1 day but not important to know when.

Time dependant, precise details of when the event happened are important.
eg. waiting lines in a que

Object Oriented Simulation: Using UML

Visual Simulation: Graphical display of computerised results and may include animation

Simulation page 165

Simulation is the appearance of reality

A technique for conducting experiments (eg. what-if analysis) with a computer on a model of a management system.

One of the most commonly used methods DSS methods

Problem solving search methods page 162

Analaytical techniques: Use a mathematical formula to derive an optimal solution directly or to predict a certain result. Used primarily for solving structured problems, usually of a tactical or operational nature. areas such as resources allocation, inventory mgt

Algorithms: Step-by-step process for obtaining an optimal solution. Solutions generated and tested for possible improvements. The process continues until no further improvment is possible.

Blind Searching: In conducting a search, a description of a desired outcome may be given. This is called a goal. A set of possible steps leading from the initial consitions to the goal is called the search steps. Problem solving is odne by searching through possible solutions.

Heuristic Searching: Via knowledge, common sense, rule of thumb. Heuristics are the informal, judgemental kowl,edge of an application area that constitute the rules of good judgement in the feild. Domain KNowledge.

Goal Seeking page 161

What-if analysis page 160

Sensitivity analysis page 159

Methods of handling multiple goals page 159

With some methods, the decision maker needs to search the solution space for an alternative that provides the required attainmemt of all goals while searching for an efficient solution.

Utility theory : Utility theory is an attempt to infer subjective value, or utility, from choices. Utility theory can be used in both decision making under risk (where the probabilities are explicitly given) and in decision making under uncertainty (where the probabilities are not explicitly given). More...

Goal programming : It can be thought of as an extension or generalisation of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved. More...

Expression of goals as constraints

A points system

Multiple goals page 158

Managers want to attain simultaneous goals, some of which may conflict.

Difficulties of analysing multiple goals:

• Difficult to obtain explicit of the orgs goals
• Decision mkaer may change importance of assigned to specific goals over time or for different decision scenarios
• goals viewed differently at different levels of the org
• goals change in response to change in the org and environment
• relationships between alternatives and their role in determinning goals may be difficult to quantify
• complex probs are solved by groups of decision mkaers, each of whom has a personal agenda
• participants view the importance / priorities of goals differently

Linear Programming pge 154

Every LP problem is composed of:

• decision variables (who's values are unknown and are searched for),
• an objective function (a linear mathematical function that relates the decision variables to the goal, measures goal attainment, and is to be optimised),
• objective function coefficients (unit profit or cost coefficients indicating the contribution to the objective of one unit of a decision variable),
• constraints (expressed in the form of linear inequalities or equalities),
• capacities (upper, lower limits of variables), and
• input/output coefficients.

The components of DSS Mathematical problems page 151

All models are made up of four basic components. Mathematical relationships links these components together.

Result (outcome) variables

• Reflect the level of effectiveness of a system, ho well the system attains its goals. These variables are outputs.
• Considered dependant

Decision variable (pg 58, 152)

• Describes alternative courses of action. The decision mkaer controls the decision variables.

uncontrollable variables (or parameters)

• Factors that affect result varibales but are not under the control of the decision maker.
• Some of these variables limit the decion maker and therefore form what are called constraints of the problem.

intermediate result variables

• Reflect intermediate outcomes in mathematical models. eg. determining machine maintenance scheduling, spoilage, total profit, employee satisfaction

Decision Trees page 149

Alternative representation of the desicion table.

http://mindtools.com

Shows the relationships of the problems graphically and can handle complex situations in a compact form.

Can be cumbersome if there are many alternatives

Treating Risk

The most common method for solving this analysis problem is to select the alternative with the greatest expected value. This approach dangerous becuase even an infintisimal chance or catastrophic loss can make the gains seem reasonable. But what happens if that small chance occurs?

Treating uncertainty page 149

Optimistic approach: assumes the best possible outcome of each alternative will occur and then selects the best.

Pessimistic approach: assumes that the worst possible outcome for each alternative will occur and selects the best of these.

Another approach is to simply assume that all states of nature are equally possible.

Decision making under risk page 144

Probabilistic

• From wiki: is a way of expressing knowledge or belief that an event will occur or has occurred. More...

Stochastic

• from wiki: A stochastic process is one whose behavior is non-deterministic in that a system's subsequent state is determined both by the process's predictable actions and by a random element. More...

Decision maker must consider several possible outcomes for each alternative, each with a given probability of occurance.

Decision making under uncertainty

the decision maker considers situations in which several outcomes are possible for ech course of action.

does not know, cannot estimate the probability of occurance of the possible outcomes.

Insufficient information

involves assessment of the decision maker's attitude towards risk.

Decision making under certainty

It is assumed that complete knowledge is available so that the decision maker knows the outcome of each course of action will be (deterministic environment).

The decision maker is viewed as being a perfect predictor of the future because it is assumed that there is only one outcome for each alternative.

Occurs most often with structured problems.

Knowledge Clasification Page 143

Decision situations are often clasified on the basis of what the decision maker knows or believes about the forecasted results.

• Certainty
• Risk
• Uncertainty

When models are used, any of these conditions can occur and different kinds of models are appropriate for each case.

qualitative models

http://www.indiana.edu/~socpsy/papers/QualEncyclo.htm

Quantitative models

Quantitative models

http://openlearn.open.ac.uk/mod/resource/view.php?id=209082

http://en.wikipedia.org/wiki/Quantitative_analyst

A DSS can include multiple models

sometimes dozens, each of which represents a different part of the decision making problem.

Each model may either be native to the DSS or integrated, interfaced

Forecasting / Predictive Analysis

Essential for construction and manipulating models because when a deciion in implemeneted, the results usually occur in the future.

Ecommerce has created an immense need for forecasting and an abundance of available information for performing it.

Many orgs have accurately predicted demand for products and services using a variety of quantitive and qualitive methods.

CRM and revenue management systems rely heavily on forecasting techniques / predictive analysis

Variable Identification

Identification of a models variables

• decision
• result
• uncontrollable

Environmental Scanning and analysis page 138

Monitoring, scanning and interpreatation of collected data.

From wiki:
Environmental scanning is a process of gathering, analyzing, and dispensing information for tactical or strategic purposes. The environmental scanning process entails obtaining both factual and subjective information on the business environments in which a company is operating or considering entering. more...

Data Collection, Problems, and Quality

* Methods for collecting raw data

• Manually or by instruments and sensors
• Surveys
• Scanners

* Data problems
* Data quality

• Contextual DQ
• Intrinsic DQ
• Accessibility DQ
• Representation DQ

* Data integrity

• Uniformity
• Version
• Completeness check
• Conformity check
• Genealogy check or ‘drill down’

* Data access and integration

• Data integration software

Data Aquisition: The nature and sources of data

Data, Information, & Knowledge

• Data – items about things
• Information – data that have been ‘massaged’ (organised/maniupulated)
• Knowledge – information, experience, learning, expertise

Internal data

• Stored in more than one place
• About people, products, services, and processes
• Available via
• Intranet
• Other internal network

External data

• Many sources
• Usually irrelevant to specific MSS
• Needs to be monitored and captured in context to business needs and operations

Personal data

• Users’ own expertise, knowledge, opinions, interpretations

self assessment: Data Warehouse

Self Assessment Questions/Discussion Topics

1. Define a data warehouse, and list some of its characteristics.
2. What is the difference between a database and a data warehouse?
3. Describe OLAP.
4. Discuss the relationship between multiple sources of data, including external data, and the data warehouse.
5. Explain the relationship between SQL and a DBMS
6. Describe multidimensionality and explain its potential benefits for MSS/DSS.

On-Line Analytical Processing

Four Main Characteristics of OLAP

• Use multidimensional data analysis techniques
• Provide advanced database support
• Provide easy-to-use end user interfaces
• Support client/server architecture

OLAP Architecture

• OLAP Graphical User Interface (GUI)
• OLAP Analytical Processing Logic
• OLAP Data Processing Logic

OLAP systems are designed to use both operational and Data Warehouse data.

Multidimensional Analysis

Common decision maker requirements:

• Summarised information
• Ability to ‘slice and dice’ information
• Display information
• View information over time

Multidimensional Data Analysis

• Data viewed as part of a multidimensional structure
• Allows users to consolidate or aggregate data at different levels
• Allows business analyst to easily switch business perspectives

Additional Functions of Multidimensional Data Analysis

• Advanced data presentation functions
• Advanced data aggregation, consolidation, and classification functions
• Advanced computational functions
• Advanced data modeling functions

Twelve Rules That Define a Data Warehouse

1. Data Warehouse and operational environments are separated

2. data are integrated

3. contains historical data over a long time horizon

4. snapshot data captured at a given point in time

5. subject-oriented

6. mainly read-only

7. development is data driven; the classical approach is process driven

8. contains data with several levels of detail

9. characterized by read-only transactions to very large data sets

10. traces data resources, transformation, and storage

11. metadata are a critical component of this environment

12. contains a charge-back mechanism for resource usage

Characteristics of the Data Warehouse

The Data Warehouse is a database that provides support for decision making.

In simple terms, a data warehouse (DW) is a pool of data produced to support decision making; it is also a repository of current and historical data of potential interest to managers throughout the org.

Integrated

• Integration is closely related to subject orientation. Data warehouses must place data from from different sources into a consistent format. To do so they must deal with naming conflicts and discrepancies among units of measure. A data warehouse is assumed to be totally integrated.

Subject-Oriented

• Data are organised by detailed subject such as sales, products or customers, containing only information relevant for decision support. Enables users to determin not only how the business is performing but why. differs from an operation database in that most operationAL databases have a product orientation and are tuned to handle transactions that update the database. Comprehensive view of the organisation.

Time Variant (time series) (built-in time aspects)

• Maintains historical data. Does not necessarily provide current status (except in real time systems). They detect trends, deviations long-term relationships for forecating and comparrisons, leading to decision making. There is a temporal quality to every data warehouse. Time is the one important dimension that all data warehouses must support. Data for analysis from multiple sources contain multiple time points (daily, weekly, monthly).

Non-Volatile (can't be changed)

• After data are enetered into a data warehouse, users cannot change or update the data. Obsolete data are discarded, and changes are recorded as new data. Enables the data warehouse to be tuned almost exclusively for data access.

Summarized
Not normalized
Sources
Metadata (Data about data)

Strategic (DSS) Data and Operational Data

Three Main Areas in Which Strategic (DSS) Data Differ from Operational Data

• Time span
• Granularity
• Dimensionality

Decision Support Systems - Main Components :: page 92

Components of a DSS are:

• Data management
• Model management
• User Interface management
• Knowledge-based management

an arrangement of computerized tools used to assist managerial decision making

• requires extensive data “massaging” to produce information.
• used at all levels within an organization
• interactive and provides ad hoc query tools

• External data
• operational data
• business data
• data store
• data extracting and filtering
• end user query tool
• business model data
• end user presentation tool

Operational Data vs. Strategic (DSS) Data

• operational data are stored in a relational database
• data storage is optimized
• operational data capture daily business transactions
• Strategic data give tactical and strategic business meaning to the operational data

Data Visualization

Technologies supporting visualization and interpretation

• Digital imaging, GIS, GUI, tables, multidimensions, graphs, VR, 3D, animation
• Identify relationships and trends

Data manipulation allows real time look at performance data

Knowledge Discovery in Databases

Data mining used to find patterns in data

• Identification of data
• Preprocessing
• Transformation to common format
• Data mining through algorithms
• Evaluation

Tools and Techniques

Data mining

• Statistical methods
• Decision trees
• Case based reasoning
• Neural computing
• Intelligent agents
• Genetic algorithms

Text Mining

• Hidden content
• Group by themes
• Determine relationships

Data Mining

• Organizes and employs information and knowledge from databases
• Statistical, mathematical, artificial intelligence, and machine-learning techniques
• Automatic and fast
• Tools look for patterns

1. Simple models
2. Intermediate models
3. Complex Models

OLAP - Online analytical processing

Activities performed by end users in online systems

• Specific, open-ended query generation

1. SQL

• Ad hoc reports
• Statistical analysis
• Building DSS applications

Modeling and visualization capabilities

Special class of tools

• DSS/BI/BA front ends
• Data access front ends
• Database front ends
• Visual information access systems

Business Analytics

Business Analytics focuses on effective use of data and information to drive positive business actions. The body of knowledge for this area includes both business and technical topics, including concepts of performance management, definition and delivery of business metrics, data visualization, and deployment and use of technology solutions such as OLAP, dashboards, scorecards, analytic applications, and data mining.

tut

Using the powerpoint slides and the text as resources to help you, answer the following six questions about the model used in activity 1 above:

1. If someone asked you what type of decision-making model this was, how would you describe it?
2. Does this modelling tool (influence diagrams) cater for multiple goals?
3. Is your model allowing for uncertainty? if so, how?
4. Is this decision probabilistic? Explain why or why not.
5. What are heuristics?
6. Does this modelling tool allow for Heuristics? Explain

Model-based management systems

Provide MSS and DSS based upon models of the organisation and its components.

Makes use of software that allows model description and organization with transparent data processing.
Capabilities

• MSS / DSS user has control
• Flexible in design
• Gives feedback
• GUI based
• Reduction of redundancy
• Increase in consistency
• Communication between combined models

Relational model base management systems

• Virtual file
• Virtual relationship

Object-oriented model base management system

• Logical independence

Database and MIS design model systems

• Data diagram, ERD diagrams managed by CASE tools

Simulations page 165

• Imitation of reality
• Allows for experimentation and time compression
• Descriptive, not normative
• Can include complexities, but requires special skills
• Handles unstructured problems
• Optimal solution not guaranteed

• Methodology

1. Problem definition
2. Construction of model
3. Testing and validation
4. Design of experiment
5. Experimentation
6. Evaluation
7. Implementation
8. Explore problem at hand
9. Identify alternative solutions
10. Can be object-oriented
11. Enhances decision making
12. View impacts of decision alternatives

Find-by-search approaches

These are analytical techniques (algorithms) for finding possible outcomes for structured problems

• A general step-by-step search for solutions that eventually obtains (finds) an optimal solution.

Blind searching
Complete enumeration search
All alternatives explored
Incomplete search
Partial search

• Achieves satysficing of particular goal
• May obtain optimal goal

Heurisitic searching
Repeated, step-by-step searches
Rule-based, so used for specific situations
Will find a “good enough” solution, but eventually will obtain optimal outcome
Examples of heuristics
Tabu search
Remembers and directs toward higher quality choices
Genetic algorithms
Randomly examines pairs of solutions, chooses best solutions so far and mutates them to find even better solutions

Sensitivity, what-if, and goal seeking analyses page 159

Sensitivity analysis

• Assesses the impact of change in inputs or parameters on solutions
• Allows for adaptability and flexibility
• Eliminates or reduces variables
• Can be automatic or trial and error

What-if analysis

• Assesses solutions based on changes in variables or assumptions

Goal seeking analysis

• Backwards approach, starts with goal
• Determines values of inputs needed to achieve goal
• E.g. break-even point determination

Satisfaction of multiple goals page 150

• One decision-making process must try to meet more that one goal.
• Simultaneous goals are often conflicting – achieving one goal makes it harder to achieve the other(s).
• Try to determine a single measure of effectiveness that judges the achievement of all goals.
• Sometimes very difficult to do.

A decision situation in which alternatives are evaluated with several sometimes conflicting goals.

Methods:

• Utility theory.
• Goal programming.
• Linear programming with goals as constraints.
• Point allocation systems.

Mathematical programming page 153

(Not to be confused with computer programming).

Use of defined and verified mathematical processes (algorithms techniques etc.) to determine best outcomes.

• Linear programming
• MinMax techniques
• Game theory

Is a family of tools designed to solve managerial problems in which the decision maker must alloate scarce resources among competing activities to optimise a measurable goal.

• Tools for solving managerial problems
• Useful when decision-maker must allocate resources amongst competing activities.
• Allocation of resources to achieve optimised specific goals.

Mathematical models

Use mathematical techniques to link decision variables, uncontrollable variables, parameters, and result variables together:

• Decision Variables have values that describe choices.

• Uncontrollable Variables have values that are outside the decision-maker’s control.

• Parameters are fixed factors (constants).

• Intermediate Result Variables are intermediate outcomes.

• Result Variables are outcomes dependent on chosen decision variables, uncontrollable variables, parameters and intermediate results.

Quantitative models

• Quantitative relationships.
• Variables involved in the model have numerical values.
• Outcomes are numerical.

“if the sales this year are $400,000 and they increase by 10% next year, the sales will be $440,000”

Nonquantitative models (qualitative models)

• Qualitative relationships.
• Variables involved in the model have qualitative values that are represented within the model by numerical values.
• Outcomes are qualitative / numerical.

“indicate your happiness with our product on a scale of 1 (very unhappy) to 5 (very happy). …”

Results (outcomes) = f(decision variables, uncontrollable variables, parameters, intermediate results)

Modeling with decision trees

A graphical representation of a decision.

• A graphical representation of relationships between variables, values and outcomes.

• Each pathway along the ‘branches’ of the tree represents a particular sequence of criteria leading to a particular outcome.

• A multiple criteria approach.

• Can model probabilistic decision-making.

• Cumbersome if there are many alternative outcomes.

Modeling with decision tables

A tabular representation of a decision.

wiki: http://en.wikipedia.org/wiki/Decision_table

Can model multiple criteria decisions

Features include:

• Decision variables (alternatives)
• Uncontrollable variables
• Result variables

Applies principles of certainty, uncertainty, and risk

Modeling with spreadsheets page 145

see Modelling and Analysis.ppt

Spreadsheets are a computerised mathematical and data management tool.

• An end-user modeling tool.
• Flexible and ‘easy’ to use.
• Supports complex mathematical methods.
• Can be used to implement linear programming techniques.
• Supports complex statistical and regression analysis methods.
• Provides services like what-if analysis, data-base management, automation macros.
• Can be used for static and dynamic modeling.
• risk analysis can be incorporated
  

Models can be developed and implemented in a variey of programming languages and systems.

Spreadsheets include extensive forecatsing, statistical and other modeling and databse management capabilities, functions and routines. As spreadsheet packages eveolved, add-ins were develpoed for structuring and solving specific model classes.

DSS related add-ins

Most popular end user modeling tool, incorporates many power financial statistical, mathematical and other functions.

Spreadsheets can perform model solution tasks such as:

1. Linear programming
Informally, linear programming determines the way to achieve the best outcome (such as maximum profit or lowest cost) in a given mathematical model and given some list of requirements represented as linear equations.
from wiki: http://en.wikipedia.org/wiki/Linear_programming

2. Regression analysis
regression analysis refers to techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables.
from wiki: http://en.wikipedia.org/wiki/Regression_analysis

Other features:

• what-if analysis

http://office.microsoft.com/en-au/excel/HA102431641033.aspx
http://office.microsoft.com/en-us/excel/CH010004551033.aspx
http://www.informit.com/podcasts/episode.aspx?e=B4C8DE7D-3AC1-45CD-8F4A-1F31714AA61A
http://www.sskkii.gu.se/publications/Documents/html/dynwhatif/

• goal-seeking: Indicating a taget cell,its desired value and changing a cell.
  
• datamanagement
• programmability (macros)

Most OLAP systems ahev the look and feel of advance spreadsheet software.

Most spreadsheet packages provide seemless integration beciase they read and write common file structures and easily interface with databases.

Modeling with influence diagrams

A graphical representation of a decision.

• Shows the various variables in a decision-making problem, their types and the relationships between them.
• Provides an understanding of the relationships (dependencies) between variables involved in a decision.
• Can be developed to any level of detail
• Shows the effect of changes in controlled variables (but not the size of the effect).

Uses a standardised set of symbols to represent variables and dependencies.

Shapes indicate variables and their types

Probabilistic decision-making

Making a decision under risk.

• Several outcomes could occur
• Each outcome has a probability of occurring
• Each outcome has a positive or negative value (payout) associated with it.

Risk analysis

• Calculate the value of the outcome for each alternative.
• Calculate the probability of each outcome occurring.
• Calculate the expected value of the outcome for each alternative. (= probability x value)
• Select the alternative with the best expected value.

Reliablility

Certainty:

• Assume complete given knowledge
• All potential outcomes known
• Best outcome determined easily
• Can be very complex

Uncertainty:

• Incomplete given information
• Several ‘acceptable’ outcomes for each decision
• Probability of occurrence of each outcome unknown
• Assess risk and willingness to take it
• Pessimistic/optimistic approaches

Decision-making

Optimum outcome

• The absolute best of all possible outcomes.
• Is the most desirable solution to a problem.
• May be difficult or impossible to discover.
• What are the criteria for judging the quality of the outcome?

Satisfactory outcome – satisfycing.

• The best of all determinable outcomes.
• Is an ‘at least satisfactory’ solution to a problem.
• May be easier to discover than the optimum.
• What is a determinable outcome?
• What are the criteria for judging the quality of the outcome?
• When is a satisfactory solution acceptable?

Reliability of a decision?
How sure can we be that a chosen outcome is the correct / best one?

Dynamic modeling

Explores a situation as it varies over time.

• Models changing situations and varying conditions
• Outcomes are time dependent (they may vary with time)
• Explores the impact of trends in the driving variables

Represents scenarios that change over time. year profit and loss projection in which the input data, such as costs and prices and quantities, change from year to year..

Time dependant : How may checkouts should be open in the supermarket.
Demands forecasted over time
Dynamic simulation
use, represent, generate trends and patterns over time

Static modeling page 142

Explores a situation at a single ‘steady’ instant (state).

Assumes the context of the problem is unvarying – or that it can be assumed to be so for simplicity.

Takes a snapshot of the situation - everything occurs at a single interval. For eg a decision to make a certain product is static, quarterly or annual income statement is static.

Static decision making are presumed to repeat with identical conditions.

Dynamic behaviour can be represented by multiple trials of the static model at separate moments.

Categories of models pg 140

Category: Optimisation of problems with few alternatives

• Process & Objective: Find the best solution from a small number of alternatives
• Representative Techniques: Decision Tables & Decision Trees

Category: Optimisation via algorithm

• Process & Objective: Find the best solution from a lareg number of alternatives, using a step by step improvement process.
• Representative Techniques: Linear and other mathematical programming models, network models.

Category: Optimisation via an analytical formula

• Process & Objective: Find the best solution in one step using a formula.
• Representative Techniques: Some inventory models.

Category: Simulation

• Process & Objective: Finding a good enough solution or the best among the alternatives checked using experimentation.
• Representative Techniques: Several types of simulation.

Category: Heuristics

• Process & Objective: Find a good enough solution using rules.
• Representative Techniques: Heuristic programming, expert systems.

Category: Predictive models

• Process & Objective: Predict the future for a given scenario.
• Representative Techniques: Forecasting models, Markov analysis

Category: Other models

• Process & Objective: Solve a what if case using a formula.
• Representative Techniques: Financial modeling, waiting lines.

DSS model types

Generate a possible outcome and present it in a useful way.

Generational strategies
(ways to create a modeled outcome)

• Algorithmic: Algorithmic information theory is a subfield of information theory and computer science that concerns itself with the relationship between computation and information.

• Statistical: A statistical model is a set of mathematical equations which describe the behavior of an object of study in terms of random variables and their associated probability distributions. If the model has only one equation it is called a single-equation model, whereas if it has more than one equation, it is known as a multiple-equation model.

• Linear programming: Some industries that use linear programming models include transportation, energy, telecommunications, and manufacturing. It has proved useful in modeling diverse types of problems in planning, routing, scheduling, assignment, and design.

• Simulation

Presentation strategies
(ways to present a modeled outcome)

• Graphical
• Quantitative
• Qualitative

Modeling and Analysis

Allows for the rapid exploration of several (many) alternative solutions

Each ‘trial’ (run / execution / analysis) of a model explores a particular set of circumstances and generates a likely outcome.

Trials of a model can be repeated (usually with the same outcomes).

A fundamental aspect of DSS methodology

Many classes of models

• specialised techniques for each type

Some DSSs may incorporate several (multiple) models (hybrid)

Trials of a model can be repeated (usually with the same outcomes)

Decision Support Methodology

Match each of the quotations with the corresponding decision-making phase according to Simon's model, note that there may not be a one-to-one mapping. For each phase justify your selection in less than 200 words.

a) "The successful decision maker remains open to the full array of alternative solutions."


b) "Until a decision has degenerated into work and reaches the stage of actual execution, for all intents and purposes there is no decision."

c) "If you have no alternative to fall back on, you begin to drift if the decision doesn't work out."

d) "Organised tracking of progress, results and feedback are non-negotiable elements of any effective action program."

e) "Only by taking the time to investigate what the decision really needs to be about can the decision maker distinguish between the symptom and the ailment."

f) "One does not make unnecessary decisions any more than a good surgeon does unnecessary surgery."

Major purpose of models

Essentially, models are designed to help decision makers make optimal (or most satsfactory) decisions.

Planning a model should always be from the perspective of ensuring that correct decisions are made, and by utilising the most efficient methods.

Human Influence on modeling

Model planning, design and construction is often inadvertently influenced by the decision-maker’s own

• cognitive style
• decision style
• pre-existing biases and preferences

Modeling is a major tool of the Choice Phase

Choice is made by:

• Obtaining the outcomes from each ‘trial’ of the model,

• Checking these outcomes against the optimal (or satisfactory) criteria

• Confirming that these criteria have not changed!

• Selecting the ‘trial’ whose outcome best fulfills the criteria.

Modeling as a process

after the ‘Intelligence’ Phase (problem definition, information gathering, collating and analysing), models are …

• designed
• developed
• tested and amended
• Implemented
• User acceptance
• User training

Why use models?

• simplify exploration by allowing an emphasis on some aspects (of reality) while ignoring others.

• possible to compress years of data into a single model,

• help to better understand complex real situations,

• cheaper to play with and experiment with model than using the real thing,

• Experimenting with ‘reality’ may not be reversible,

• Simulation models are suited to ‘what-if?’ scenarios,

• can be re-run with different states and data,

• economical ‘trial and error’ testing is facilitated

Types of Models

Physical models

• Physical (touchable) representations.
• A smaller or larger physical copy of an object.

Abstract models

• Generalisations of reality that remove specific details in favour of general information which is relevant for the purpose.

Object models

• Represent, describe and simplify the appearance, structure, organisation and functioning of things (objects).

Process models

• Represent, describe and simplify the dynamic nature of processes.

Descriptive Models

• Describe the reality as it is or as it is believed to be.
• E.g. mathematical models (see below).
• Useful for satisficing.

Mathematical Models

• Describe reality using mathematical language and techniques, often variables and systems of equations that relate outputs to inputs.

Simulations

• Computerised models of dynamic processes.

What is Modeling? What is a model?

The use of a representation of something to explore the real thing.

• A simplification of the ‘reality’.
• A representation of the ‘reality’.

Choosing to do nothing!

One option that is always present, but often overlooked, is the choice to do nothing.

It should be placed on the list of ‘what-if’ scenarios, and evaluated along with the intentional alternatives as one possible choice.

The Knowledge-Based approach to decision-making

Is an extension of the classic approach, and may be:

• Descriptive knowledge (e.g. spreadsheet, balance sheet, etc.)
• Procedural Knowledge ( a series of steps, for example)

Decision-making

A problem-solving process that has been preceded by a problem-finding process

The means through which a manager seeks to achieve a desired result.

Finding and choosing between alternatives.

Alternatives
Where do they come from?
How many are sufficient?
How many are too many?

How much effort should be expended in finding more alternatives?
How to control them?
How to analyse them effectively?

How to select the optimum alternative?

Decision-Making versus Problem Solving?

It is tempting to regard only Simon’s ‘choice’ stage as decision-making, with the other stages being classified as problem solving.

Essentially, all four of Simon’s phases are incorporated within both decision-making and problem solving.

• Decision-making requires problem solving.
• Problem-solving requires decision-making.

In this unit of study we use the terms synonymously.

Decision making and problem solving are intimately integrated into each other.

Simon’s (4) Implementation Phase

“putting a recommended solution to work.”
Turban et al (2007) pg. 69.

• a “new order of things”
• introduces change which needs to be managed

Simon’s (3) Choice Phase

“the actual decision is made and the commitment to follow a certain course of action is made.”
Turban et al (2007) pg. 68.

Described as the critical act of decision-making

• Decision is made

1. Choose the optimum / ‘best’ course of action

• Commitment given to decision

Phase includes:

• Search for
• Evaluation of
• Recommendation of
• the optimum / ‘best’ course of action

Simon’s (2) Design Phase

“finding or developing and analyzing possible courses of action.”
Turban et al (2007) pg. 57.
Involves

• finding
• developing
• analysing
• alternative courses of action
• Testing courses of action
• modeling
• determining outcomes of each possible course of action

Simon’s (1) Intelligence Phase

“the decision maker examines reality and identifies and defines the problem. Problem ownership is established as well.”
Turban et al (2007) pg. 53.

• Problem Identification via

1. Scanning - Gathering information & data

1. External sources
2. Internal sources

• Problem Classification
• Problem decomposition
• Problem ownership

Herbert Simon’s model

Four phases of decision-making:

Intelligence Phase –
identify the problem
“the decision maker examines reality and identifies and defines the problem. Problem ownership is established as well.”

Design Phase
build a model to generate alternatives
“finding or developing and analyzing possible courses of action.”

Choice Phase
develop and choose a solution
“the actual decision is made and the commitment to follow a certain course of action is made.”

Implementation Phase
implement the solution
“putting a recommended solution to work.”

Decision-making as an organised & structured process:

Providing decision support in an organised and effective way assumes that decision-making can be modelled and analysed as a general, structured process.

If so, a generalised model of decision-making can become a basis for a systematic approach to decision support.

E.g. Herbert Simon’s ‘model’ of decision-making.

What type of support is needed:

Recognise the nature of the decision.
context?
binary (yes / no)
fuzzy / uncertain
Irreversible
Optimal / satisfactory (see satisficing)

Recognise the importance of the decision.
potential risks / benefits
consequences

Recognise the role and responsibility of the decision-maker.

Recognise the factors which may influence the decision-making process:
decision style,
cognition,
management style,
personality,
and others ...

Recognise the information / data that is needed to support the decision.

Recognise the criteria to be used to choose the optimum (or best) outcome.

How support is provided

If decision-making is a process of choosing among alternatives to achieve a goal …

• What type of support is needed?
• How can we provide the support required?

What is decision support?

• Helping decision-makers make better (?) decisions.

• Better = ‘better outcomes’ = faster, more reliable, safer, higher chance of success, known consequences, less risk, recordable, etc.

• Providing tools and resources to maximise the opportunity to make a good decision.

• Techniques, information, models …

• Helping decision-makers choose the best (?) alternative from a range of alternatives.

• Intelligent systems? -- Making the decisions for the decision-makers.

Decision-making

A problem-solving process that has been preceded by a problem-finding process.

The means through which a manager seeks to achieve a desired result.

The choice of the optimal solution to a problem.
or
the choice of a satisfactory solution to a problem.

What are decisions?

'A decision is made when we choose one alternative from a set of two or more alternative solutions to a particular problem.'

Three aspects of making a decision:

• We have a problem;
• We have a set of two or more alternative solutions to the problem;
• We choose one of these alternatives as THE solution to the problem.

Whenever we make a decision, we are using some criteria to evaluate each of the alternatives so that we can choose the best one. Sometimes we may not be aware what the criteria are, but they are always there.

We now have four aspects of making a decision:

• We have a problem;
• We have a set of two or more alternative solutions to the problem;
• We have a criteria to use to provide a comparative 'value' for each of the alternatives;
• We choose one of these alternatives as THE solution to the problem based on its value according to the criteria.

Every decision is an attempt to choose a solution to a problem.
A decision is only meaningful if there are at least two alternative solutions to choose from.

To make an effective choice between the alternatives we need to establish some criteria to use to compare the alternatives.

Using the criteria we can give a value to each of the alternatives.

By comparing the values of each alternative we can choose the best one.

The alternative we choose is our solution to the problem, it is our decision.

Automated Decision Making (ADS) pg 17

Operations research approach (OR) / Management Science

1. Define the problem (decision situation with difficulty or opportunity)
2. Classify the problem into a standard category
3. Construct a model that describes a real world problem
4. Identify possible solutions to the modelled problem and evaluate solutions
5. Compare, choose and recommend a potential solution to the problem.

Differs to Simon's Decision Making Process because t seeks to classify a problem into a standard category.

Based on mathematical modelling which transforms a world problem into an appropriate prototype.

Simon's decision making process pg 15

1. Define the problem (decision situation with difficulty or opportunity)
2. Classify the problem into a standard category
3. Construct a model that describes a real world problem
4. Identify possible solutions to the modelled problem and evaluate solutions
5. Compare, choose and recommend a potential solution to the problem.

Operational Decisions, Managerial Decisions, Strategic Decisions

Examples of each in my life:

Operational: Menu Planning - What will be eaten, where will the ingrediants be purchased and when will purchasing occur happen.

Managerial: Who will do the shopping, how much can spent?

Strategic: Keeping frozen dinners

What are the three categories of managerial roles?

Interpersonal

Figurehead: Symbolic head, obliged to perform a number of routine duties of a legal or social nature.

• Queen of England

Leader: Responsible for the motivation and activation or subordinates; staffing, training and associated duties.

• Richard Branson

Liason: Maintains a self developed network of outside contacts and informers who provide favours and information.

• Liason Manager with industry and govt to keep abreast etc

Informational

Monitor: Seeks and recieves a wide variety of special information (much of it current) to develop a thorough understanding of the organisation and environment; emerges as the nerve centre of the orgs internal and external info.

Disseminator: Transmits information recieved from outsiders or subordinates to members of the org, some of it factual, some of it requiring interpretation, interrogation.

Spokesperson: Transmits information to outsiders about the orgs plans, policies, actions, results. Serves as an expert on the orgs industry.


Decisional

Entrepeneur: Searches the org and environment for opportinuties and initiates improvement projects to bring about change.

Disturbance Handler: Responsible for corrective action when the org faces important, unexpected disturbances.

Resource Allocator: Allocation of org resources of all kinds, approval of all significant org decisions.

Negotiator: Reps org at major negotiations.

Why is DSS hard to define? Umbrella term page 21

It is a context free expression that means different things to different people depending on the type of DSS they use.

The term DSS can be used as an umrella term to describe any computerised system that supports decision making in an organisation.

An organisation may have knowledge management systems.......etc

Hybrid Systems

The combination of 2 or more system types.

With customising, a system may be used either as an EIS or an MIS, for example.

Many different types of DSS combined into a Hybrid System

Impacts of the World Wide Web on DSS technologies. pg 95

Summary of DSS Functions

To help management answer questions such as:

• What is?
• Why?
• What will be?
• Why?
• Which is the optimum solution?

Technologies for Decision-Making Processes

Structured (Programmed) : MIS, Management Science Models, Transaction Processing

Semistructured : DSS, KMS, GSS, CRM, SCM

Unstructured (Unprogrammed) : GSS, KMS, ES, Neural networks

Factors affecting Decision-Making

New technologies and better information distribution have resulted in more alternatives for management.
So many new technologies have presented so many different tools, strategies and alternatives. This affects decision making becuase there is alot more to consider.

Complex operations have increased the costs of errors, causing a chain reaction throughout the organisation
An error in a decision can ripple through an organistion very quickly these days.

Rapidly changing global economies and markets are producing greater uncertainty and requiring faster response in order to maintain competitive advantages.
The world moves fast and decision must be made quickly.

Increasing governmental regulation coupled with political destabilization have caused great uncertainty.
The need to be ready for anything - govt mandates

Decision Support Frameworks

Structured Decision, Semistructured Decision, Unstructured Decision

How did DSS originate?

• Originally purely ‘number-crunching’
• Followed by simple spreadsheet and database applications (Visicalc and dBase 2)
• Leading to complex spreadsheet and database applications
• After which came interaction with end-users
• And, currently being incorporated, fuzzy logic, artificial intelligence, etc.

Decision Support Systems (DSS)

• A sub-set of MSS
• For individuals or groups (GDSS)
• Designed to anticipate the questions and needs of the user
• May be specific or general and customisable
• May be used for:

- Current situations
- Forecasting
- Producing alternative solutions
- Grading those alternatives

Why do Management Roles need Decision Support?

Decision support = helping managers make better decisions.

• A wrong decision can have far-reaching consequences.
• No one person can remember (and recall) everything
• Frequently some form of calculating will be necessary
• A decision may be needed urgently

What form of support is needed?

Information that…

• is accurate
• is up-to-date
• is easy to assimilate
• is relevant
• has quick and easy access to specific records

Management Support Systems

Management Support System = MSS.

MSS = The application of technologies to support the performance of management tasks in general.

Sometimes applied more narrowly to Decision Support Systems (DSS) or Business Intelligence (BI).

Caution – MSS, DSS & BI are all sometimes used as context-free umbrella terms**.

Decision Support Systems and Business Intelligence can be considered as subsets of MSS.

Managerial Roles & Mintzberg’s 10 Management Roles

• Interpersonal
• Informational
• Decisional

Interpersonal

• Figurehead
• Leader
• Liaison

Informational

• Monitor
• Disseminator
• Spokesperson

Decisional

• Entrepreneur
• Disturbance Handler
• Resource Allocation
• Negotiator

Management functions

Management as an ‘interface of responsibility’ between a business unit / group / team and other business units or the business’ external environment.

The manager holds the responsibility for the performance of the business unit.

Management functions include:

• Receipt of requirements and interpretation of these into performance targets and schedules.
• Allocation of human and non-human resources.
• Monitoring of performance and behaviour.
• Improving performance and behaviour.
• Collection, summarising and reporting of performance measures.
• Troubleshooting
• Others ??

Business environment factors that create pressure on orgs pg 7

Markets

• Competition
• Global expansion
• booming electronic markets
• innovative marketing methods
• opportunities for outsourcing
• need for real time, on demand transactions

Consumer Demands

• Desire for customisation
• Desire for quality, diverstity of products, speed of delivery
• Customers getting more powerful and less loyal

Technology

• More innovations, new products and services
• Increasing obsolescence rate
• Increasing information overload

Societal

• Growing govt regs and deregs
• work force more diversified, older, composed of more women
• Prime concerns of homeland security and terrorist attacks
• increasing social responsibility of orgs

Business Pressures-Responses-Support model pg 6

has 3 components

• Business pressures that result from business climate
• Responses (Actions taken) to counter pressures (or take advantage of opportunities)
• Computerised support that facilitates monitoring of environment and enhances response actions

Decision Support Systems [DSS] Defined

“A decision support system is a computer based system that is used personally on an ongoing basis by managers and their immediate staff in direct support of managerial activities”

“The term Decision Support System (DSS) has been widely used to refer to systems that are computer-based aids for decision making. Over the years, the term has come to refer to systems that can lend support to decision makers involved in solving problems of some complexity”

“Business Intelligence (BI) Systems are Information Systems that assist managers with unstructured decisions by retrieving and analyzing data in order to identify, generate and interpret useful information. A BI system possesses interactive capabilities, aids in answering ad hoc queries, and provides data and modelling facilities, generally through the use of Online Analytical Processing (OLAP) tools, to support nonrecurring, relatively unstructured decision making.”

discuss at an introductory level how information technology can be used to assist managerial decision-making;

• Speedy computations
• Improved communications and collaboration
• Increased productivity of group members
• Improved data management
• Mnaging giant data warehouses
• Quality support
• Agility support
• Overcomming cognitive limits in processing and storing information
• Using the web
• Anywhere anytime support

Highly suitable for structured and semi structured problems.

Learning Objectives

Lesson 1

• discuss at an introductory level how information technology can be used to assist managerial decision-making;

• distinguish between strategic, operational, and tactical decisions;

• discuss at an introductory level who makes decisions within an organisation and how these decisions are typically implemented,

• discuss at an introductory level the importance of Internet and Web-based technologies in the implementation of decision support systems.

Lesson 2

• describe various decision styles and factors that influence decision-making;

• discuss several of the more important capabilities of decision support systems;

• name and discuss the major components of decision support systems;

• discuss, using some specific examples, the types of models used for decision support systems;

• name and describe the purpose of each phase of Simon's methodology and how those phases relate to the use of a decision support system.

Decision Making LO

• explain** what a decision is and what decision-making is;
• discuss** why decision-making is a significant role for people involved in business management activites;
• list and describe** some of the important factors involved in making decisions;

** by explain or discuss or describe I mean write or say something that will help another person understand the concepts.

Decision Support Methodology LO

• name and describe each of Simon's four phases of decision-making,
• discuss how decision style, cognition, management style, personality and other factors influence a decision-making process,
• begin to discuss how support for decision-making can be provided in practice through the use of different types of models that can be used throughout Simon's 'design phase'.

Modelling & Analysis LO

• identify and describe some typical components that make up a Management Support System ,
• discuss, with examples, different types of models.

Lesson 3 Business Intelligence

• Describe issues in data collection, problems, and quality
• Begin to describe the characteristics, organization and use of business data
• Begin to explain how the web impacts data collection, organisation and presentation
• Explain the importance and use of a data warehouse
• Begin to understand the tools used for business intelligence

Business Analytics LO

• define the concepts Business Analytics
• begin to understand the use of technology solutions in the use of Business Analytics

Data Warehousing LO

• define the concepts of the Data Warehouse
• begin to understand the concepts of On-Line Analytical Processing (OLAP)

Data Acquisition LO

• clearly differentiate between data, information, and knowledge
• discuss several data quality and integrity issues