BCA Modeling Decision Process and Decision Support System Notes Study Material

BCA Modeling Decision Process and Decision Support System Notes Study Material

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BCA Modeling Decision Process and Decision Support System Notes Study Material
BCA Modeling Decision Process and Decision Support System Notes Study Material

BCA Modeling Decision Process and Decision Support System Notes Study Material

Modeling Decision Process

Before knowing about modeling decision process, we should know about its basic.

Decision: The thought process of selecting a logical choice from the available options, is called decision. When trying to make a good decision, a person must weight the positives and negatives of each option, and consider all the alternatives. For effective decision-making, a person must be able to forecast the outcome of each option as well, and based on all these items, determine which option is the best for that particular situation.

1. Decision Making

  1. Decision making is a crucial part of good business. The question then is that ‘how is a good decision made?’
  2. One part of the answer is good information, and experience in interpreting information. Consultation i.e., seeking the views and expertise of other people also helps, as does the ability to admit one was wrong and change one’s mind.
  3. There are also aids to decision making, various techniques which help to make information clearer and better analysed, and to add numerical and objective precision to decision making (where appropriate) to reduce the amount of subjectivity.
  4. The process of examining your possibilities options, comparing them, and choosing a course of action.

2. Decision-Making in Organizations

The process of choosing a course of action for dealing with a problem or opportunity.

Decision-making levels:

Decision Making Levels
Decision-Making Levels

1. Steps in Systematic Decision making

  1. Recognize and define the problem or opportunity.
  2. Identify and analyze alternative courses of action, and estimate their effects on the problem or opportunity.
  3. Choose a preferred course of action.
  4. Implement the preferred course of action.
  5. Evaluate the results and follow up as necessary.

2. The Significance of Decision making

  1. Decision making is the one truly distinctive characteristic of managers.
  2. Decisions made by top managers commit the total organization toward particular courses of action.
  3. Decisions made by lower levels of management implement the strategic decisions of top managers in the operating areas of the organization.
  4. Decisions invariably involve organizational change and the commitment of scarce resources.

The Conventional Decision Support System (DSS) Making Process

Conventional DSS making process
Conventional DSS-making process

1. Decision Modeling

Decision modeling refers to the use of mathematical or scientific methods to determine an allocation of scarce resources which improves or optimizes the performance of a system. The terms operations research and management science are also used to refer to decision modeling

1. Advantages and Disadvantages of Modeling

  1. Advantages

(i) Less expensive than custom approaches or real systems.

(ii) Faster to construct than real systems

(iii) Less risky than real systems

(iv) Provides learning experience (trial and error)

(v) Future projections are possible

(vi) Can test assumptions

  1. Disadvantages

(i) Assumptions about reality may be incorrect

(ii) Accuracy of predications often unreliable

(iii) Requires abstract thinking

2. Modeling Decisions

Modeling Decisions
Modeling Decisions

2. Types of Decision Process Model

Organizational level decisions use some models which involve several managers. Problem identification and solution involve many departments, multiple view points and even organizations which are beyond the scope of an individual manager. Six different types of organizational decision making processes have been identified:

  1. The PLUS decision making model
  2. Rational planning model
  3. Management science approach
  4. Carnegie model
  5. Incremental decision process model
  6. Garbage can model

1. The Plus Decision-making Model

The traditional decision-making model taught in most ethics programs is beyond the reading comprehension level of an estimated 25% of the employee population. We need an alternative model capable of ensuring that the ethical issues inherent in routine business situations could be effectively surfaced while making the model easy to use by people who were functionally semi-illiterate. The steps of PLUS decision making model are given below:

Step 1: Define the problem PLUS.

Step 2: Identify alternatives.

Step 3: Evaluate the alternatives PLUS.

Step 4: Make the decision.

Step 5: Implement the decision.

Step 6: Evaluate the decision PLUS.

PLUS decision-making model
PLUS decision-making model

2. Rational Planning Model

The rational planning model is the process of realizing a problem, establishing and evaluating planning criteria, creating alternatives, implementing alternatives, and monitoring progress of the alternatives. It is used in designing neighborhoods, cities, and regions. The rational planning model is central in the development of modern urban planning and transportation planning. The very similar rational decision-making model, as it is called in organizational behavior is a process for making logically sound decisions. This multi-step model and aims to be logical and follow the orderly path from problem identification through solution.

Rational planning model
Rational planning model

The Six-step rational decision-making model:

  1. Define the problem.
  2. Identify decision criteria
  3. Weight the criteria
  4. Generate alternatives
  5. Rate each alternative on each criterion
  6. Compute the optimal decision

3. Management Science Approach

This model is analog to the rational approach by individual decision maker and came into being during World War II. Mathematical and statistical techniques were applied to large-scale military problems that were beyond the ability of individual decision-makers. This system is applied to problems that are analyzable, measurable, and can be structured in a logical way.

Amongst is limitations, It can not sense qualitative data like competitor reactions, customer taste, product warmth etc. that can not be incorporated in any mathematical model.

An overview of management science approach
An overview of management science approach

4. Carnegie Model

Organization level decision-making involve many managers and that final choice is based on a coalition among the managers, rather than by the one at the top based on information fed to them. A coalition is an alliance among several managers and stakeholders (managers from line depts, staff specialists, powerful customers, union leaders, bankers, external groups etc.) who agree about the organizational goals and priorities. Two reasons why coalitions are made:

  1. Organizational goals are often ambiguous and operative goals of the departments are inconsistent.
  2. Managers do not have time, resources and mental capacity to identify all dimensions and process all information for decision-making.

Under this model the decisions are made to satisfice rather than optimize problem solutions. The coalition will accept a solution that is perceived as satisfactory to all coalition members.

Managers are concerned with immediate problems and their immediate solutions. They don’t expect a perfect solution in a conflict-laden and ill defined situation.

One of the best and most visible coalition builders of recent times was George W Bush who sought a broad-based coalition before the start of the war in Iraq to gain agreement for his vision of a “new world order”.

Choice process in carnegie model
Choice process in carnegie model

5. Incremental Decision Process Model

Most of the organization choices are a series of small choices (series of nibbles) that combine to produce major decisions (big bite). They move through several decision points and may hit barriers (decision interrupts). Case of firing of a TV/Radio announcer. Three major stages have been identified.

  1. Identification phase (problem flagged by complaints from viewers, advertisers, colleagues).
  2. Development stage (what the organization had done last time or what is done by other similar organizations).
  3. Selection phase (make a choice from available options; in case of difference of opinion bargaining takes place and may have to take recourse to Carnegie model).

Finally the decision needs to be authorized by the competent authority.

Incremental decision making process (Henry Mintzberg)
Incremental decision making process (Henry Mintzberg)

6. Garbage Can Model

It deals with the pattern or flow of multiple decisions within the organization that experience extremely high uncertainty about growth and change. Such a state is called “Organized Anarchy” and are not guided by the vertical hierarchy but by the following factors:

  1. Problem preferences (goals, problems, alternatives and ambiguity at every stage).
  2. Unclear or poorly understood technology (explicit database that facilitates decision-making not available).
  3. Turnover (experiences high attrition end and the past experience brought on the table is fluid).
Garbage Can Model of decision making
Garbage Can Model of decision making

Applications of Decision Models

A sample of systems to which decision models have been applied, includes:

  1. Financial Systems: Portfolio optimization, security pricing (e.g., options, mortgage-backed securities), cash flow matching (e.g., pension planning and bond refunding).

Example: Liberty view capital management uses a spreadsheet optimization model developed by a 1995 Columbia MBA to hedge bond investments using stock and options.

  1. Production Systems: Oil, steel, chemical, and many other industries.

Example: Citgo uses linear programming to improve refining operations. Total benefit: approximately $70 million annually.

  1. Distribution Systems: Airlines, paper, school systems and others.

Example: Westvaco, a fortune 200 paper company, uses linear programming to optimize its selection of motor carriers. The result: 3+6% savings on trucking costs of $15 million annually. This work was done by a 1992 Columbia MBA.

  1. Marketing Systems: Sales force design, forecasting new product sales, telecommunications strategies, brand choice, merchandising strategies.
  2. Graduate School Admissions

Example: The director of CBS admissions uses linear programming to aid in the admissions process.

Overview of Decision Models
Overview of Decision Models

Need of Knowledge Management

(i) Periodically for an entire organization or subunit

(ii) At the start of a KM project

(iii) At the end of a KM project

Decision Support Systems (DSS)

Offer potential to assist in solving both semi-structured and unstructured problems. Decision support systems are computer-based information systems that provide interactive information support to managers and business professionals during the decision-making process.

1. Definitions of DSS

Gorry and Scott-Morton (1971): Management Decision Systems-Interactive computer-based systems, which help decision-makers utilize data and models to solve unstructured problems.

Keen and Scott-Morton (1978): Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision-makers who deal with semi-structured problems.

2. Basic Themes of DSS

  1. Information systems,
  2. Used by managers.
  3. Used in making decisions.
  4. Used to support, not to replace people.
  5. Used when the decision is “Semistructured” or “Unstructured.”
  6. Incorporate a database of some sort.
  7. Incorporate models.

3. Need /Importance of DSS

  1. Increasing complexity of decisions

(i) Technology

(ii) Information:

“Data, data everywhere, and not the time to think!”.

(iii) Number and complexity of options.

(iv) Pace of change.

  1. Increasing availability of computerized support

(i) Inexpensive high-powered computing.

(ii) Better software.

(iii) More efficient software development process.

  1. Increasing usability of computers.

4. DSS Benefits

  1. Improving personal efficiency.
  2. Expediting problem solving.
  3. Fascilitating interpersonal communications.
  4. Promoting learning or training.
  5. Increasing organizational control.

5. DSS as a System

  1. Man-Machine System: DSS is man-machine system for decision making purposes. Man part is more open and probabilistic while the machine part is more closed and deterministic, e.g., DSS for deciding Price and Adverting levels.
  2. Closed-loop system with feedback external to system: DSS uses feedback to adjust output. Feedback is not internal like an elevator. The user providers judgmental inputs to DSS.
  3. DSS components: Database, model base, knowledge base, interface which interact with each other and the user.

Decision support system’s use:

(i) Analytical models.

(ii) Specialized databases.

(iii) Decision maker’s own insights and judgments.

(iv) Interactive, computer-based modeling process to support the making of semistructured and unstructured business decisions.

6. Decision-Making as a Component of Problem Solving

Decision-making as a component of problem solving
Decision-making as a component of problem solving

7. Solution Types

  1. Optimization model: Finding the best solution
  2. Satisficing model: Finding a good but not necessarily the best solution to a problem.
  3. Heuristics: Commonly accepted guidelines or procedures that usually find a good solution

8. Problem-Solving Factors

  1. Multiple decision objectives.
  2. Increased alternatives.
  3. Increased competition.
  4. The need for creativity.
  5. Social and political actions.
  6. International aspects.
  7. Technology
  8. Time compression.

9. Characteristics of a DSS

  1. Handles large amounts of data from different sources.
  2. Provides report and presentation flexibility.
  3. Offers both textual and graphical orientation
  4. Supports drill down analysis.
  5. Performs complex, sophisticated analysis and comparisons using advanced software packages.
  6. Supports optimization, satisficing, and heuristic approaches.
  7. Performs different types of analyses.

(i) “What-if” analysis.

(a) Makes hypothetical changes to problem and observes impact on the results

(ii) Simulation

(a) Duplicates features of a real system.

(iii) Goal-seeking analysis.

(a) Determines problem data required for a given result.

10. Capabilities of a DSS

1. Supports

  1. Problem-solving phases
  2. Different decision frequencies

BCA Modeling Decision Process and Decision Support System Notes Study Material

2. Highly Structured Problems

Straightforward problems, requiring known facts and relationships.

3. Semi-structured or Unstructured Problems

Complex problems wherein relationships among data are not always clear, the data may be in a variety of formats, and are often difficult to manipulate or obtain.

11. Web-Based Decision Support Systems

Decision support system software provides business intelligence through web browser clients that access databases either through the Internet or a corporate intranet.

DSS Components: Decision support systems rely on model bases as well as databases as vital system resources. A DSS model base is a software component that consists of models used in computational and analytical routines that mathematically express relationships among variables.

Examples include:

(i) Spreadsheet models

(ii) Linear programming models

(iii) Multiple regression forecasting models

(iv) Capital budgeting present value models

12. Data Mining for Decision Support

The main purpose of data mining is knowledge discovery, which will lead to decision support.

1. Characteristics of Data Mining Include

  1. Data mining software analyzes the vast stores of historical business data that have been prepared for analysis in corporate data warehouses.
  2. Data mining attempts to discover patterns, trends, and correlations hidden in the data that can give a company a strategic business advantage.
  3. Data mining software may perform regression, decision-tree, neural network, cluster detection, or market basket analysis for a business.
  4. Data mining can highlight buying patterns, reveal customer tendencies, cut redundant costs, or uncover unseen profitable relationships and opportunities.

13. Components of a DSS

  1. Model Management Software (MMS): Coordinates the use of models in the DSS.
  2. Model base: Provides decision-makers with access to a variety of models.
  3. Dialogue manager: Allows decision makers to easily access and manipulate the DSS.
An overview of componenting of DSS
An overview of componenting of DSS

Steps in Constructing Decision Support System and its Role in Business

There are following steps which are constructing the DSS.

  1. Identification of the problem: In this stage the developer and the knowledge engineer interact to identify the problems. The following points are discussed:

(i) The scope and extent are analyzed.

(ii) The return of investment analysis is done.

(iii) The amount of resources needed is identified.

(iv) Areas in the problems that can give much trouble are identified and a conceptual solution of that problem is found.

(v) Over all specification is made.

  1. Decision about mode of development: Once the problem is identified, the immediate step would be to decide about the vehicle for development. He can develop shell for development by any programming language. In this stage various shells and tools are identified and analyzed for their suitability. These tools whose features fit the characteristics of the problems are analyzed in details.
  2. Development of a prototype: Before the development of a prototype, we decide the knowledge level to solve the particular problem. For this, we adopted some methods in sequence. After this the taste of knowledge begins the knowledge of Engineer and developer which interact frequently and domain specific knowledge is entranced. When knowledge representation scheme and knowledge is available a prototype is constructed.
  3. Prototype validation: The prototype under goes the process of testing for various problems and revision of the prototype takes place. It is very important step the DSS.
  4. Planning for full scale system: In prototype construction, the area in the problem that can be implemented with relative case is first choice extensive planning is done. Each subsystem development is assigned a group leader and schedules are drawn.
  5. Final implementation: Maintenance and evaluation: This is the final stage of DSS Life Cycle. The full scale system developed is implemented at the basic resources requirements are fulfilled and parallel conversion.

Model Base

Provides decision makers with access to a variety of models and assists them in decision making.

1. Models

  1. Financial models
  2. Statistical analysis models
  3. Graphical models
  4. Project management models

2. The DSS Hierarchy

  1. Suggestion systems
  2. Optimization systems
  3. Representational models
  4. Accounting models
  5. Analysis information systems
  6. Data analysis systems
  7. File drawer systems

1. File Drawer Systems

  1. They are the simplest type of DSS.
  2. Can provide access to data items.
  3. Data is used to make a decision.
  4. ATM Machine.
  5. Use the balance to make transfer of funds decisions.

2. Data Analysis Systems

  1. Provide access to data.
  2. Allows data manipulation capabilities.
  3. Airline Reservation system.
  4. No more seats available.
  5. Provide alternative flights you can use.
  6. Use the info to make flight plans.

3. Analysis Information Systems

  1. Provide access to multiple data sources.
  2. Combines data from different sources.
  3. Allows data analysis capabilities.
  4. Compare growth in revenues to industry average- requires access to many sources.
  5. The characteristic of the recent “datawarehouse” is similar.

4. Accounting Models

  1. Use internal accounting data.
  2. Provide accounting modeling capabilities.
  3. Can not handle uncertainty.
  4. Uses bill of material.
  5. Calculate production cost.
  6. Make pricing decisions.

5. Representational Model

  1. Can incorporate uncertainty.
  2. Uses models to solve decision problem using forecasts.
  3. Can be used to augment the capabilities of accounting models.
  4. Use the demand data to forecast next years demand.
  5. Use the results to make inventory decisions.

6. Optimization Systems

  1. Used to estimate the effects of different decision alternative.
  2. Based on optimization models.
  3. Can incorporate uncertainty.
  4. Assign sales force to territory.
  5. Provide the best assignment schedule.

7. Suggestion Systems

  1. A descriptive model used to suggest to the decision-maker the best action.
  2. A prescriptive model used to suggest to the decision-maker the best action.
  3. May incorporate an Expert System.
  4. Applicant applies for personal loan.
  5. Use the system to recommend a decision.

3. DSS Categories

1. Support-based DSS (Alter 1980)
  1. Data-based DSS
  2. Model-based DSS
Categories of DSS
Categories of DSS

4. Based on the Nature of the Decision Situation (Donovan & Madnick 1977)

  1. Institutional

(i) Culture of the organization

(ii) Regularly used

(iii) Used by more than one person

  1. Add hoc

(i) One of kind

(ii) One-time use

(iii) Used by single individual

5. Based on Number of Users (Keen 1980)

  1. Individual, Multi-individual, Group

BCA Modeling Decision Process and Decision Support System Notes Study Material

6. Detrimental DSS Effects

  1. Design flaws.
  2. Inadequate understanding of task or user.
  3. Inadequate modeling of ‚Äúreality”.
  4. Inadequate understanding of human information processing constraints.
  5. Can promote cognitive biases.

BCA Modeling Decision Process and Decision Support System Notes Study Material

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