This document provides an overview of quantitative analysis for management decision making. It defines quantitative analysis as the collection, organization, and interpretation of numerical data. There are two main types: descriptive analysis, which summarizes and presents data, and inferential analysis, which makes inferences, tests hypotheses, and determines relationships. Quantitative analysis simplifies mass data, aids decision making, and helps identify trends. It has various uses in business for tasks like resource allocation, inventory control, project management, and risk analysis. While powerful, it also has limitations like only dealing with quantitative data and producing probabilistic rather than exact conclusions. Computers now help analyze large datasets.

1. QUANTITATIVE
ANALYSIS IN
MANAGEMENT
DECISIONS
Dr. B Dayal
Mobile No. 0941962113
Email ID: i_dayal77@yahoo.com
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Copyright © 2021
Yardstick International College

3. CHAPTER
ONE
Overview
3

4. Chapter:1
Overview of quantitative
analysis
 Introduction
 Definition
 Types of quantitative analysis
 Components of quantitative analysis
 Characteristics of quantitative analysis
 Classification of Quantitative analysis
 Functions of quantitative analysis
 Uses of quantitative analysis
 Limitations of quantitative analysis
 Computers in quantitative analysis

5. INTRODUCTION
Managers as decision makers:
• Issues:
 How much to produce
 What prices to charge
 How many staff to employ
 Whether to invest in new capital equipment
 whether to fund a new marketing initiative
 whether to introduce a new range of products
 whether to employ an innovative method of production.

6. Decisions in the Management
Functions

7. Decision Making
 Decision
○ Adhoke Decision
○ Intuitive Decision
○ Decisions based on Quantitative analysis

8. Decision Making
 Decision
 Adhoke Decision
• Based on:
• No basis
• No logic
• No facts and figures
• Result: Maximum chances of failing
○ Intuitive Decision
• Based on Intuition
• Result: Good chances of being unsuccessful.
○ Decisions based on Quantitative analysis
• Maximum chances of success

9. intuitive decision making
4–9

10. Decision Making
 Decision
○ Decision making is a process of identifying a set of
feasible alternatives and from these selecting the
best course of action. It is a technique used to find a
solution to solve problem
○ The Definition
“The process of identifying and selecting a course of
action to solve a specific problem”.
-James
Stoner
“A decision is a course of action which is consciously
chosen for achieving a desired result.”
-Haynes and

11. PROBLEMS IN DECISION MAKING
 Failure to define the problem
 Failure to understand the problem
 Complexity of problem:
 False information:
 Unable to find alternatives
 Obligations of decision maker
 Decisions influenced by others
4–11

12. Decision Making Process
 Identifying a problem
 Identify decision criteria
 Collect data pertaining to the problem.
 Developing, analyzing, and selecting an alternative
 Implementing the selected alternative.
 Evaluating the decision’s effectiveness.

13. The Decision-Making Process

14. Step 1: Identifying the
Problem
 Problem
○ A discrepancy between an existing and desired
state of affairs.
 Characteristics of Problems
○ A problem becomes a problem when a manager
becomes aware of it.
○ There is pressure to solve the problem.
○ The manager must have the authority, information,
or resources needed to solve the problem.

15. Step 2: Identifying Decision Criteria
 Decision criteria are factors relevant to resolve
the problem:
○ Costs that will be incurred (investments required)
○ Risks likely to be encountered (chance of failure)
○ Outcomes that are desired (growth of the firm)

16. Step 3: Allocating Weights to
the Criteria
● Decision criteria are not of equal importance:
● Assigning a weight to each item
● Places the items in the correct priority order of their importance.

17. Criteria and Weight in Car-Buying
Decision
(Scale of 1 to 10)
4–17
CRITERION WEIGHT
Price 10
Interior comfort 8
Durability 5
Repair record 5
Performance 3
Handling 1

18. Step 4: Developing Alternatives
● Identifying viable alternatives
○ Alternatives are listed (without evaluation) that can resolve the problem.
Step 5: Analyzing Alternatives
○ Appraising each alternative’s strengths and weaknesses
○ An alternative’s appraisal is based on its ability to resolve the issues identified
in steps 2 and 3.

19. Assessed Values of Laptop
Computers Using Decision
Criteria
6–19

20. Step 6: Selecting an
Alternative
 Choosing the best alternative
○ The alternative with the highest total weight is chosen.
Step 7: Implementing the
Alternative
 Putting the chosen alternative into action.
 Conveying the decision to and gaining commitment from those who will
carry out the decision.
6–20

21. Step 8: Evaluating the
Decision’s Effectiveness
 The soundness of the decision is judged by its outcomes.
○ How effectively was the problem resolved by outcomes resulting from the
chosen alternatives?
○ If the problem was not resolved, what went wrong?
6–21

22. INTRODUCTION
DEFINITION OF QUANTITATIVE ANALYSIS
 In its plural form, quantitative analysis stands for numerical
facts ( facts expressed in numbers) pertaining to a collection of
objects.
 In its singular form, it stands for the science of collection,
organization & interpretation of numerical facts.
 ‘The science of estimates & probabilities’. -
Boddington
 ‘Science of collection, presentation, analysis & interpretation of
numerical data.’ - Croxton
& Cowden
 ‘Quantitative analysis may be defined as an aggregate of facts
affected to a marked extent by multiplicity of causes,
numerically expressed, enumerated or estimated according to
reasonable standards of accuracy, collected in a systematic
manner for a pre-determined purpose & placed in relation to
each other.’

23. Types of Quantitative Analysis
QUANTITATIVE
ANALYSIS
DESCRIPTIVE
ANALYSIS
INFERENTIAL
ANALYSIS
COLLECTING
ORGANISING
SUMMERISING
PRESENTING DATA
MAKING INFERENCE
HYPOTHESIS TESTING
DETERMINING RELATIONSHIPS
MAKING PREDICTIONS

24. COMPONENTS OF
QUANTITATIVE ANALYSIS
- croxton & cowden
COLLECTION OF
DATA
PRESENTATION
OF DATA
ANALYSIS
OF DATA
INTERPRETATION
OF DATA

25. CHARACTERISTICS OF
QUANTITATIVE ANALYSIS
 Quantitative analysis is an aggregate of facts.
 Affected to a marked extent by multiplicity of causes.
 Numerically expressed.
 Enumerated or expressed to reasonable standards of accuracy
depending on the end use.
 Collected, mathematical modelled and interpreted in a
systematic manner.
 Related data are collected for a pre-determined purpose.
 Data are placed in relation to each other

26. FUNCTIONS OF QUANTITATIVE
ANALYSIS
 Simplifies mass data.
 Makes comparison easier.
 Brings out hidden relations between variables.
 Reduces bulk of data.
 Decision making process is eased. Adds precision to thinking.
 Guides policy formulation & aids planning.
 Brings out or indicates trends & tendencies.
 Aids studying relationship between different factors. Relation
between production & prices.
 To provide tools for scientific research
 To help in choosing an optimal strategy
 To enable in proper deployment of resources
 To help in minimizing costs
 To help in minimizing the total processing time required for
performing a set of jobs

27. USES OF QUANTITATIVE ANALYSIS
 Business and Industry.
 linear programming: Used for optimal allocation of scarce
resources in the problem of determining product mix
 Inventory control model: Useful in deciding when and how
much items are to be purchased
 CPM, and PERT: Helps in determining the earliest and the
latest times for the events and activities of a project. This helps
the management in proper deployment of resources.
 Decision tree analysis and simulation technique: help the
management in taking the best possible course of action under
the conditions of risks and uncertainty.
 Queuing theory: Used to minimize the cost of waiting and
servicing of the customers in queues.
 Replacement theory: Helps to determine the most economic
replacement policy regarding replacement of equipment.

28. A BAR DIAGRAM
0
10
20
30
40
50
60
70
80
90
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
East
West
North
SOUTH

29. PIE CHART

30. Classification of Quantitative
Techniques
 Mathematical Quantitative Techniques
 Statistical Quantitative Techniques
 Programming Quantitative Techniques
 Mathematical Quantitative Techniques:
 Permutations and Combination
 Set Theory
 Matrix Algebra:
 Determinants:
 Differentiation:
 Integration
 Differential Equation:

31. Classification of Quantitative
Techniques
 Statistical Quantitative Techniques: Statistical techniques
involve:
 Collection of data:
 Measures of Central tendency, dispersion, skewness and
Kurtosis
 Correlation and Regression Analysis
 Index Numbers:
 Time series Analysis:
 Interpolation and Extrapolation:
 Statistical Quality Control
 Ratio Analysis:
 Probability Theory:
 Testing of Hypothesis

32. Classification of Quantitative
Techniques
 Programming Techniques: Programming techniques involve:
 Linear Programming:
 Queuing Theory:
 Game Theory:
 Decision Theory:
 Inventory Theory:
 Net work programming:
 Simulation:
 Replacement Theory:
 Non Linear Programming:
 Sequencing:
 Branch and Bound Technique

33. LIMITATIONS OF QUANTITATIVE
ANALYSIS
 Does not deal with qualitative data. Only with quantitative
data.
 Cannot deal with an individual fact. Can apply to aggregate of facts.
 Quantitative conclusions/inferences are not exact. They are
probabilistic/ true only on an average.
 Quantitative techniques involves mathematical models,
equations and other mathematical expressions
 Quantitative techniques are based on number of assumptions.
Therefore, due care must be ensured while using quantitative
techniques, otherwise it will lead to wrong conclusions.
 Quantitative techniques are very expensive.
 Quantitative techniques do not take into consideration
intangible facts like skill, attitude etc.
 Quantitative techniques are only tools for analysis and
decision-making. They are not decisions itself.

34. COMPUTERS & QUANTITATIVE
ANALYSIS
 When collected data are small, the analysis and interpretation
can be done without much difficulty. But when a huge amount
of data, the process of analysis and interpretation will be
difficult.
 With the advent of computers a lot of statistical programs are
available in the market. They help us in summarizing,
presenting & analyzing the mass data in a short time. Some of
them are:
 Mminitab
 SPSS
 Texto
 Contexto
 Excel
 E-view
 MS project

35. Discussion Questions
● Download the software SPSS and discuss its utility in
decision making.
● Discuss a case where your superior has taken an
adhoke decision and it failed. How he could have
utilised quantitative analysis?
● Discuss few areas of O.R. applications in your
organization or organization you are familiar with.