Previous material review

Assignments

• Microstrategy case study

• Microsoft BI tools

Decision Making, Systems, Modeling, and Support

Decision Making: Introduction

Decision Making System includes:

• How decision making is practiced

• Some of the underlying theories

• Models of decision making

Definition of Decision making

• “It is a process of choosing among two or more alternative courses of action for the purpose of attaining a goal or goals.”

Characteristics of Decision Making• Individuals are involved• There may be 100’s of alternatives• Needs data and analysis with understanding to make a good decision• Past results may not be sufficient to predict future results• Decisions are interrelated• There may be several conflicting objectives• May decisions involve risk. Diff people have different appetite for risk• Groupthink can lead to bad decision.• Decision makers are interested in evaluating what- if scenarios.• Experimentation with a real system-trial and error-may result in failure.

Continued……

• Experimentation with a real system is possible only for one set of conditions at a time and can be disastrous.

• Changes in the decision making environment may occur continuously, lending to invalidating assumptions about a situations.

• Collecting information and analyzing a problem takes time and can be expensive.

• There may not be sufficient information to make an intelligent decision.

Decision Making And Problem Solving

• Problem occurs when: a system does not meet it’s established goals, does not yield the predicted results,or does not work as planned.

• Some consider the entire process of decision making as problem solving while other view phase 1-3 as formal decision making, ending with a recommendation.

• Additionally includes the actual implementation of the recommendation.

Decision Style

• The manner in which decision makers think and react to problems.

• Vary from individual to individual and situation to situation.

• Although the process is similar, the application is not always linear.

• As a result, people make decision in different ways.• Heuristic and analytical• Autocratic vs democratic• Consultative• Decision situation as well style for system to help.

Hence it should be flexible

Decision Making Models

“A model is a simplified representation or

abstraction of reality.”

• Iconic(Scale)Models: Physical replica of system

• Analog Models: Symbolic representation of reality.

• Mental Models: Descriptive representation of decision making situations.

• Mathematical(Quantitative)Models: Mathematical description of abstract model

Phases of The Decision Making(Decision making process)

Simon’s Four Phases of Decision Making

• Intelligence

• Design

• Choice

• Implementation

The Intelligence phase

• Involves scanning the environment, includes

several activities aimed at identifying problem

situations or opportunities.

• Intelligence phase includes:

1.Problem(or opportunity) Identification

2.Problem Classification

3.Problem Decomposition

4.Problem Ownership

The Decision Phase• Involves finding or developing and analyzing possible

courses of action.

• Includes understanding the problems and testing solutions for feasibility.

• A model of the decision-making is constructed, tested and validated.

• Models include: normative(best amongst available amongst all available), suboptimisation, descriptive model(simulation, cognitive) and satisifying

• Measuring outcome, risk and scenarios

The Choice Phase

• Choice is critical act of decision making.

• The choice phase is the one in which the actual decision is made & the commitment to follow a certain course of action.

• Includes the search for, evaluation of, and recommendation of an appropriate solution.

• A solution is a specific set of values for the decision variables in a selected alternatives.

The Implementation Phase

• Putting a recommended solution to work.

Decision Support SystemsConcepts, Methodologies, And technologies

Concept of DSS

• A system intended to support managerial decision makers in semi structured and unstructured decision situations.

• Adjuncts to decision makers to extend their capabilities but not to replace their judgments.

DSS Configuration

• Depend on nature of management-decision situation and specific technology used.

• These technologies are assembled from four basic components: Data, models, user interface, and knowledge.

• These components are managed by software that is either commercially available or programmed for specific task.

DSS Application• Built to support the solution of a certain problem

or to evaluate an opportunity.• DSS typically have their own database and are

developed to solve a specific problem or set of problems. Therefore they are called DSS applications.

• A DSS is an approach for supporting decision making.

• Uses an interactive, flexible, adaptable computer-based information system CBIS developed for supporting the solution to a specific nonstructured management problem.

Characteristic And Capabilities of DSS

Characteristics And Capabilities

• Support for decision makers in semistructured and unstructured problems.

• Support for all managerial levels.

• Support for individuals and groups.

• Support for Interdependent or sequential decision.

• Support in all phases of decision making process.

• Adaptable and flexible.

continued…..

• User friendly, Interactive.

• Improvement of effectiveness rather than efficiency.

• Support and not to replace the decision maker

• Easy development by end users.

• Models are used to analyze decision making.

• Data access

• Can be employed as standalone tool or can be integrate with other DSS.

Components of DSS

A DSS application composed of:

• The Data Management Subsystem

• The Model Management Subsystem

• The User Interface Subsystem

• The Knowledge-Based Management System

The Data Management Subsystem

Data management subsystem is composed of

following elements:

• DSS database: Is a collection of interrelated data, organized to meet the needs and structure of an organization, used by one or more applications.

• DBMS: A database is created, accessed , and updated by DBMS.

• The Directory: Is a catalog of all data in a database.

• The Query Facility: Necessary to access, manipulate, and query data.

• Key issues: quality, scalability, security, integration

The Model Management Subsystem

The model management subsystem is composed

of following elements:

• Model base: Contains routine & special statistical, financial, forecasting, management science, and other quantitative models.

• Types: strategic, operational, tactical, analytical.

• The Model Base Management System: Interrelating models with appropriate linkage through a database.

• Model Directory: Catalog of all the models and other software in the model base.

• Model Execution, integration, and Command

The User Interface Subsystem

• Covers all aspects of communication between a user and the DSS or any MSS.

• It includes not only the hardware and software but also factors that ease to use, accessibility, and human-machine interactions.

• It is the source of many of the power, flexibility, and ease-of-use characteristics of MSS.

• The user interface is managed by software called the user interface management system.

The knowledge based management subsystem

• Use to get the solution of complex unstructured and semistructured problems.

• Supply the required expertise for solving some aspects of the problem

• Provide knowledge that can enhance the operation of other DSS components.

• The knowledge component consists of one or more intelligent systems.

The Decision Support System User

• The person or people primarily responsible for

making decision, provides expertise in guiding

the development and use of a DSS.

• Two broad classes of users:

1.Managers

2.Staff specialists

Includes Financial analysts, production

planners, and market researchers.

Decision Support System Classification

Classification categories are as follows:

• Communication-driven and group DSS

• Data-driven DSS

• Document-driven DSS

• Knowledge-driven DSS, data mining, and

• management ES applications

• Model-driven DSS

• Compounded (hybrid)