Mgt Science Introduction Lecture1

8/7/2019 Mgt Science Introduction Lecture1

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The traditional name of Management Science is OperationResearch and these two terms are used interchangeably. Thisname was applied because the teams of scientists in World War

were doing research on how to manage military operations.

Management science is based strongly on some scientific fields,including mathematics and computer science. It also draws on the

social sciences, especially economics.

Definition of Management Science:

Management Science is the application of scientific methods to

the analysis and solution of managerial decision-makingproblems.


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The Characteristics of Management Science

1. A primary focus on managerial decision-making.

2. The application of the scientific approach to decision-making.3. The examination of the decision situation from a broad perspective.

4. The use of methods and knowledge from several disciplines.

5. A reliance on mathematical and other quantitative models.

6. The extensive use of electronic computers.

It is clear that the main theme of management science is to help in

decision-making. To help the decision making process different modelor approaches are developed by many scholars. These are collectivelycalled Managerial Science.

Since the consequence of decision-making is unpredictable, thats whythis science is developed to lessen the errors in decision-making.


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Decision making process can range from Structured Process

(programmed) to Unstructured (non-programmed) Process.

Decision making process itself divided into three phases:

(i) Intelligence: that means searching for conditions that call fordecisions.

(ii) Design: refers to inventing, developing and analyzing possiblecourses of action.

(iii) Choice: selecting a course of action from those alternatives.


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Types of Decision Making:

(a)Structured Process: It is used for routine and repetitiveproblems for which standard solution procedure exists. A fully

structured problem is one, where all the three phases that is,intelligence, design and choice, are structured. In a structuredprocess, procedures are standardized, objectives are clear andinput and output are clearly specified.

(b) Unstructured Process: In an unstructured process, none ofthe three phases, that is, intelligence, design and choice, arestructured. In an Unstructured Problem, human intuitions are stillthe basis for decision making. Typical Unstructured Problem suchas planning of new services to be offered and hiring an executive.

(c) Semi-structured Process: In a Semi-structured process, some,but not all, of the phases that is, intelligence, design and choiceare structured. The semi-structured problem fall betweenstructured and unstructured, involving a combination of both

standard solution procedures and individuals judgment.


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Managerial activities are categorized into three broadcategories:

(a) Strategic Planning: this involves planning long-term goals

and policies for resource allocation.

(b) Management Control: this involves activities relating toacquisition and efficient allocation / utilization of resources inthe accomplishment of organization goals

(c) Operation Control: this involves efficient and effectiveexecution of specific tasks or routine task.

Decision Making and Support

Several tools/ techniques were developed to support managerialdecision-making. The nature of support provided by the varioustools are shown in the following table (next slide):


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This table illustrates that:

Structured decision or problem is supported by managementscience models and other quantitative models such as, statistical,

financial and accounting models.

For semi-structured and unstructured decision or problem,

conventional management science models or other quantitativemodels, are insufficient. Such situation requires, use of SupportiveInformation System, such as, Decision Support System (DSS) andExpert System (ES).

Therefore, management science models or techniques are appliedto only Structured problem or decision.


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Types of Model: A system has three parts, input, process andoutput. A system or a problem can be represented through model.A model is a simplified representation or abstraction of reality. It

is simplified because reality is too complex to copy exactly.Different types of models ,are as follows:

(i) Iconic (scale) Model: A physical replica of a system on adifferent scale than the original. For example car or bridge or

model.(ii) Analog Model: Not a real model but behaves like it. Forexample two dimensional charts or diagrams, organizationalchart, map etc.

(iii) Mathematical Model: When the physical representationbecomes more complex, a more abstract model is used with theaid of mathematics. Most of the Managerial Science analysis isexecuted with the aid of mathematical models. It is a process thattranslates observed phenomena into mathematical expressions.


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Classification of Mathematical Models:(a)Optimization Models - are used to find the best solutionunder a set of restrictions and scarce resources.

(b) Prediction models -are usually provide important inputto optimization models to predict future event.

(c)Classification by type of data / information available:

Deterministic models are used in decision under certainty,i.e. complete information is available regarding futureevents. Stochastic models (probabilistic) are used indecision making under risk where the chances of

occurrence of future events are known.


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(iv) Static Model: A single snapshot of a situation. For example A monthly schedule of employees work hours prepared at thebeginning of the month

(v) Dynamic Model: It is used to evaluate scenarios that changeover time. Dynamic models show trends and patterns of overtime.Dynamic models are therefore time dependent. For example return on investment for next five years.


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The Management Science ProcessManagement Science process can be described by the followingfour step procedure.


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Usually, parts of this process must be repeated and revised whennew data or information become available, and when assumptionsare revised .

(i) Observation - Identification of a problem or opportunity that

exists (or may occur soon) in a system or organization.

(i) Definitionof the Problem - problem must be clearly and

consistently defined, showing its boundaries and noting whatits symptoms are. Often, what is described as a problem (e.g.

excessive cost) may only be a Symptom of a problem (e.g.

improper inventory levels). Because real life problem are

complicated by many interrelated factors thus sometimes it is

difficult to distinguish between symptoms and problems. While

defining a problem, it needs to be classified or conceptualized

into a definable category such as structured and unstructured

are two major category. Decision situation are also need to be

classified on the basis of data available about the situation.


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If decision maker has complete information about future event,this is known as, decision making under certainty or deterministicsituation.

When the decision maker knows the chances or probability ofoccurrence of future event, this is known as, decision makingunder risk or probabilistic or stochastic decision situation.

If decision maker does not know or cannot estimate theprobability of occurrence of future event, this is known as,decision making under uncertainty.


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(iii) Model Construction - Development of the functional

mathematical relationships that describe the decision variables,

objective function and constraints of the problem.

All mathematical models are comprises of three basiccomponents. They are: Decision variable, Result variable and

uncontrollable factors.

(i) Decision Variables this are controllable or manipulable by thedecision maker. Such as quantities of products to produce or thenumber of units to order etc.

(ii) Result Variables: are dependent variables, depends on theoccurrence of the decision variables and the uncontrollable

factors. Such as output of the system, outcomes, payoffs etc.

(iii) Uncontrollable Factors: In any decision situation, there arefactors (variables, constants or parameters) that affect the resultvariables but are not under the control of decision maker. For

instance, prices of goods, raw material required to produce a unit.


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(iv) Model Solution - Models solved using management science

techniques.

Choose an Appropriate Solution Technique

An optimization algorithm- is step by step process ofsearching for an optimal solution by gradually improving eachsolution.A heuristic algorithm- is a step-by-step procedure or rules, ina finite number of steps, to arrive a optimum solution.

Generate Model Solutions

Test/Validate Model Results=Is the solution reasonable?=Are radical changes needed?=Does it fit present and future plans?=Unacceptable results? Return to modeling.

PerformWhat--If

Analyses


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(v) Model Implementation -Actual use of the model or its

solution.


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Mgt Science Introduction Lecture1

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