quantitative techniques in business case study

8/11/2019 Quantitative Techniques in Business Case Study

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QUANTITATIVE

TECHNIQUES INBUSINESS CASE

STUDY

Submitted by:

TULIAO, Maria Athena E.

Submitted to:

Prof. Febre


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1.Case Study of Air Conditioner

Market

INRODUTION OF THE CASE

The Case study is about Indian Air conditioner

Industry.

Some brief of the Indian consumer durable

industry. The total market size is of around

Rs.250,000 million. The Home Appliance Industrysize is around Rs 87,500 million. Among them

Refrigerator contribute the largest share of Rs

38000 million. followed by room air conditioners

at around Rs 27,500 million. And washing machine

at Rs 14,000 million.

Among them Air conditioner Industry enjoys thehighest growth of 20% per year. Due to reduction

in excise duty and a decline in import duties

narrowed down the gap between organized andunorganized sector.

The Share of Unorganized market was 70% in

1980 has dropped down and is now 25%.Increasing in the disposable income and change in

the life style are the major factors that supports the

demand of the air conditioner industry.


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The graph shows the market Share of the air conditioners

in the different segments.

The another graph shows the market share in the

different region in India

SHARES,

DOMESTI

C, 20, 20%

SHARES,

GOVERMNT, 15,

15%

SHARES,

CORPORA

TES/INDU

STRIES,20, 20%

SHARES,

SMALL

PRIVATE

SECTOR,25, 25%

SHARES,

HOSPITAL

S, 5, 5%

SHARES,

PUBLIC

SECTOR,

15, 15%

Chart TitleDOMESTIC

GOVERMENT

CORPORATES/INDUSTRIES

SMALL PRIVATE

SECTOR

HOSPITALS

PUBLIC SECTOR

SHARE,

NORTH,

37, 37%

SHARE,

EAST, 8,

8%

SHARE,

WEST,

33, 33%

SHARE,

SOUTH,22, 22%

SHARE IN DIFFERENT REGION

NORTH

EAST

WEST

SOUTH


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Here the air conditioner in Organized and UnorganizedSector in windows and Split Air conditioner market share

is given below.

Major Players in the Market

An Increase in the market has allowed various major

players to participate in the race for maximizing their

own market share . Blue Star, LG, Voltas ,Carrier,

Amtrex Hitachi, Samsung, National etc are some of the

major players in the market.

The Indian Air conditioner Industry is growing rapidly.

Now to Analyze the market. We have to go for Sampling.

, Window

ac, 75, Split ac,

85

, Window

ac, 25, Split ac,

15


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INTRODUCTION OF THE SAMPLING

"Sampling" basically means selecting people/objects from a

population. The researcher generally takes sample portion of

the whole population for the study. The process of selecting

the sample from the population is called the sampling.

Reasons For Sampling

Sampling can save money.

Sampling can save time.

For given resources, sampling can broaden the scope of

the data set.

Because the research process is sometimes destructive,

the sample can save product.

If accessing the population is impossible; sampling is

the only option.

Sampling Procedure

In the design of the sampling process there are 5 steps.

1.Target population must be defined.

2.

Sampling Frame must be determined.3.Appropriate sampling technique must be selected.

4.

Sample size must be determined.

5.

Sampling process must be executed

In the reference to the case of Air conditioner industry

in India.


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SAMPLING FRAME

Sampling Frame is a list that possesses the informationabout the subjects that a researcher wants to research.

Our Sampling frame is taken to be Metropolitan Cities as

use of AC was found to be comparatively more in metrosthan non-metro cities. It was found to be 60% of total AC

users.

SAMPLING TECHNIQUES

Sampling techniques are broadly classified into two

Random Sampling Techniques

Non-Random Sampling Techniques

Random Sampling Techniques

In this type of sampling each member of population has equal

chance of being included in the sample.

We cannot consider this type of sampling as we cannot ask

each and every person in the city that if he uses airconditioners

or not.

So we took Non Random Sampling Techniques

Non Random Sampling Techniques

In this type of sampling techniques, the selection of sampling

units is not based on random selection.

In this type of sampling, Quota Sampling is been considered

for our study.


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Quota Sampling

In this type of sampling, certain classes such as age, gender,

income group, and education level are used as strata. A

researcher uses non-random sampling methods to gather data

from one stratum until the required quota fixed by the

researcher is fulfilled.

As all type of customers cant afford an AC, we have decided to

categorize them into strata based on their Income Group.

Probability Technique or Non Probability Technique

Probability technique in each of the population has the same

probability of being selected as a part of the sample. In random

sampling, the chance factor comes into play in the process of

sample selection. For statistical analysis a random sample is

ideal.

However in same probability technique is not applicable. In that

case non-probability technique every unit of the population does

not have the same chance of being selected. In Non Probability

Technique member of the sample are not selected by chance as

per convenient.

In these cases we use Non Probability Technique because in that

we will not consider whole city as it is heterogeneous in nature

so we select an area of a city.

SAMPLING AND NON-SAMPLING ERRORS

The error caused by theselection of asample instead of

conducting a census of thepopulation.

Sampling error has the origin in sampling itself.

1.

It can occur due to faulty selection of the sample.
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CONTROL -Select the sample which is true

representative of the population.

2. Sometime researchers demarcate sampling units

wrongly and hence provide scope for committing

sampling errors.

CONTROL -Selecting a sample randomly, sampling

errors can be computed and analysed very easily.

Researcher objective must be clear.

NON-SAMPLING ERROR

Errors occur due to forces generally present in every

research. These error arises at the stages of observation,ascertainment and processing of data.

1. Faulty designing and planning of survey

The questionnaries is the primary sources of the

data collection. Some time this data collection do

not match with the objectives

No trained and qualified staff for survey.

CONTROL: By hiring the trained staff and using

proper questionnnearies. This error can bereduced.

2. Response error-

Some times respondents do not provide pertinent

information during the survey.

CONTROL: Try to reduce the bias of the

interviewer.

3. Error in coverage:

When the objectives of the research are not clearlydefined , the possibilities are always high that few

sampling units that should not have been included

are included in the sample list.

CONTROL: Objective must be clear

4. Compiling error

A researchers can commit error during

compilation of the data. Various operations of the

data processing, such as editing and coding of the


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response, tabulation, and summerization of the

data. Errors can occur during presentation and

printing of the results.

CONTROL:

Employing trained and experienced personnel.


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2. CASE ON MANUFACTURING

PERSONAL COMPUTERS IN NIGERIA

Micro-company is nigeria based manfacturer of personalcomputer. The company is planning to build a new

manufacturing and distributing facility in port-harcourt, Aba,

lagos, Abuja, and kaduna. It will take approximately five years to

build the necessary infrastructure and put it into operation. The

company has estimate the facility cost in millions in each city.

ECONOMIC CLIMATE

Cities Decline same improvePort-harcourt 21.7 19.1 15.2

Aba 19.0 18.5 17.6

Lagos 19.2 17.1 14.7

Abuja 22.5 16.8 13.8

Kaduna 25.0 21.2 12.5

Determine the best decision using the following decision criteria

(a) MAXIMIN (b)MINIMAX (c)HURWITZ(=0.4)

(d)Equally likelihood

SOLUTION(a)MAXIMIN(criterion of pessimism)

Cities Decline same improve

minimum in

row

Port-harcourt 21.7 19.1 15.215.2


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Lagos 5.8 4.1 2.9

5.8

Abuja 2.5 4.4 3.8

4.4

Kaduna 0.0 0.0 5.1

5.1

The minimum in row is 3.3 which implies that the best decision is

to build the facility in port-harcourt.

(c)HURWITZ(criterion of realism) =0.4

Hurwitz = (maximum in row) + (1-

)(minimum in row)

Hurwitz(port) =0.4(21.7) + (1-0.4)(15.2) = 17.8

Hurwitz(aba) =0.4(19.0) + (1-0.4)(17.6) = 18.16

Hurwitz(lagos) =0.4(19.2) + (1-0.4)(14.7) = 16.5

Hurwitz(abuja) =0.4(22.5) + (1-0.4)(13.8) = 17.28

Hurwitz(kaduna)=0.4(25.0) + (1-0.4)(12.5) = 17.5

In this case the best option is to build the facility in Aba

(d)equally likelihood

Cities Decline same improve

average in row

Port-harcourt 21.7 19.1 15.2

18.7


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Aba 19.0 18.5 17.6

18.4

Lagos 19.2 17.1 14.7

17.0

Abuja 22.5 16.8 13.8

17.7

Kaduna 25.0 21.2 12.5

19.6

Using equally likelihood the best alternative is to build the facility

in Kaduna.

3. CASE ON AIRLINE TICKETING

As a first step towards planning new facilities at one of

its city centre ticket offices, an airline has collected data

on the length of time customers spend at a ticket desk


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(the service time). One hundred customers were

investigated and the time in minutes each one was at an

enquiry desk was measured. The data are shown below.

Classify the data in intervals one minute wide. Form a frequency

histogram. What service time is likely to be exceeded by onlyten per cent of customers.

4. Case on JP Carruthers Co.


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The JP Carruthers Co. is a medium-sized manufacturing firm. Its

sales figures are about 220 million and its employment level

has been around 1100 for the last ten years. Most of its sales are

in the car industry. JPCs profit last year was 14 480 000. They

have always enjoyed a reputation for reliability and havegenerally been regarded as being well managed.

With few exceptions JPCs direct labour force, numbering about

600, is represented by the TWU, the Transport Workers Union.

It is the practice in this industry to negotiate employee benefits

on a company-wide basis, but to negotiate wages for each class

of work in a plant separately. For years, however, thisantiquated practice has been little more than a ritual.

Supposedly, the system gives workers the opportunity to

express their views, but the fact is, the wages settlement in the

first group invariably sets the pattern for all other groups within

a particular company. The Door Trim Line at JPC was the key

group in last years negotiations. Being first in line, thesettlement in Door Trim would set the pattern for JPC that year.

Annie Smith is forewoman for the Door Trim Line. There are

many variations of door trim and Annies biggest job is to see

that they get produced in the right mix. The work involved in

making the trim is about the same regardless of the particular

variety. That is to say, it is a straight piecework operation and

the standard price is 72p per unit regardless of variety. Thework itself, while mainly of an assembly nature, is quite intricate

and requires a degree of skill.

Last years negotiations started with the usual complaint from

the union about piece prices in general. There was then,

however, an unexpected move. Here is the unions demand for

the


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Door Trim Line according to the minutes of the

meeting:

Well come straight to the point. 72p a unit is

diabolical A fair price is 80p. The women average

about 71 units/day. Therefore, the 8p more that we

want amounts to an average of 5.68 more per

woman per day. This is the smallest increase weve

demanded recently and we will not accept less than80p.

(It was the long-standing practice in the plant to

calculate output on an average daily basis. Although

each persons output is in fact tallied daily, the bonus

is paid on daily output averaged over the week. Theidea is that this gives a person a better chance to

recoup if she happens to have one or two bad days.)

The unions strategy in this meeting was a surprise. In

the past the first demand was purposely out of line

and neither side took it too seriously. This time theirdemand was in the same area as the kind of offer that

JPCs management was contemplating.


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Prior to the negotiations management had thought that seven

per cent would be a reasonable offer, being approximately the

rate at which productivity and inflation had been increasing in

recent years. Privately they had set ten per cent as the upper

limit to their final offer. At this level they felt some scheme

should be introduced as an incentive to better productivity,

although they had not thought through the details of any such

scheme.

As a result of the unions strategy, however, JPCsnegotiating

team decided not to hesitate any longer. Working late, they put

together their best package using the ten per cent criterion. The

main points of the plan were as follows:


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5. Case Study on Newspaper

Letters

The two attached letters appeared recently in anewspaper. In the first letter, Dr X concludes that

dentists should not give anaesthetics. In the second,

Mr Y concludes that dentists are thesafest

anaesthetists there are.

Danger in the Dental Chair

SirAs a medically qualified anaesthetist responsiblefor a large number of dental anaesthetics I read (June

17) with great distress and despair of the death under

an anaesthetic of Miss A.

It is a source of great concern to me that dentists are

permitted to give anaesthetics. Any fool can give an

intravenous injection, but considerable skill and

experience is needed to handle an emergency

occurring in anaesthetics. For anyone, however

qualified, however competent, to give an anaesthetic

with no help whatsoever is an act of criminal folly; the

BDA, BMA and all the medical defence societies would

agree with this.

A Dental Safety Record that Cant Be Matched


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SirDr Xs feelings (Letters, June 25) about the tragic

death of Miss A will be shared by many, and they do

him credit; but they have also led him astray. Miss A

was not anaesthetised; she was heavily sedated witha combination and dosage of drugs which produced a

severe respiratory depression which the practitioner

was

unable to reverse.

In calling for a ban upon the giving of generalanaesthetics by dentists, Dr X is on very unsafe

ground. The possession of a medical degree does not

of itself confer immunity from stupidity or

negligence; many other people would still be alive if it

did. If Dr X consults the records produced by the

Office of Population Censuses and

Surveys, he will find that, overall, more deaths

associated with dental anaesthesia occur when the

anaesthetist is medically qualified than when he is a

dentist. Excluding the hospital service (where all

anaesthetists are medically qualified but where

nearly 50 per cent of deaths occur), medically

qualified anaesthetists give 36 per cent of the dentalanaesthetics; they have 45 per cent of the associated

deaths. Not only a balance in favour of the dentist

anaesthetist, but one which shows that mischance can

occur to anyone, however skilled.

Not even Dr X, I think, would claim that all the deaths

which occurred with medically qualified anaesthetists


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were due to misadventure, and all those which

occurred with dentists were negligence.

However, these figures should be put in their proper

perspective. In general dental practice and in the

Community Dental Service, about 1.5 million

anaesthetics are given each year. Over the last 15

years, deaths have averaged 4 a year. It is a safety

record which cannot be matched by any other form of

general anaesthesia.


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The QuantitativeApproach

Quantitative information:

We can get data quickly, but we need to be sure

that we are working on the right problem and that

the data is valid.

Datameans

a few recording

an extensive national or international survey

An item of data becomes informationwhen it

informs the user.

Internet has transformed the flow and availability

of data.

The ability to manage data, produce information

and work with problems are all seen as and

important business competencies.

Desk research:

First you need checking what work has already

been done.

Provide information or identify techniques.


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METHODOLOGYOld methods New methods

Reliability and validity of findings (conclusions)

Was the purpose of the research clear?

Was this research necessary? (desk research)

Was the means of data collection appropriate?

What can we infer?

(-inductive approach generalization

-deductive approach)


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MEASUREMENT

Measurement is about assigning a value or a score to

an observation.Measurement is the representation of

type,

size or

quantity by numbers.

How we work with data will depend on the level ofmeasurement achieved.

Measurement can be categorized as:

nominal, ordinal, interval, ratio

Nominal (or categorical) level of measurement:

If responses merely classified into a number ofdistinct categories, where no order or value.

The classification of survey respondents on the basis

of

religious affinity,

voting behaviour or

car ownership.

The numbers assigned give no measure of amount

or importance.

For data processing convenience, we may code

respondents 0 or 1 (e.g. YES or NO) or


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1, 2, 3 (Party X, Party Y, Party Z), but these

numbers do not relate to meaningful origin or to a

meaningful distance.

We cannot calculate statistics (mean, standard

deviation).

We can make percentage comparisons (e.g. 30 % will

vote for party X), present data using bar charts or use

more statistical methods

Ordinal level of measurement:

has been achieved when it is possible to rank order

all categories according to some criteria.

The preferences indicated on a rating scale ranging

from strongly agree to strongly disagree or the

classification of respondents by social class

(occupational groupings A, B, C1, C2, D, E) are both

common examples where ranking is implied.

Individuals are often ranked as a result of

performance in sporting events or business appraisal.

In these examples we can position a response or a

respondent but cannot give weight to numerical

differences.

It is as meaningful to code a five point rating scale 7,

8, 12, 17, 21 as 1, 2, 3, 4, 5 though the latter is

generally expected.

Only statistics based on order really apply.

You will, however, find in market research and other

business applications that the obvious codings are


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made (e.g. 1 to 5) and then a host of computer-

derived statistics calculated.

Many of these statistics can be useful for descriptive

purposes, but you must always be sure about the type

of measurement achieved and its statistical

limitations.

Interval scale:

is an ordered scale where the differences between

numerical values are meaningful.

Temperature is a classic example of an interval scale,

the increase on the centigrade scale between 30 and

40 is the same as the increase between 70 and 80.

However, the heat cannot be measured in absolute

terms (0 oC does not mean no heat) and it is not

possible to say that 40 oC is twice as hot as 20 oC, but

we can say it is hotter.

In practice there are few business-related

measurements where the subtlety of the interval

scale is of consequence.

Ratio scale:

The highest level of measurement,- which has all the distance properties of the

interval scale and in addition,

- zero represents the abscence of the

caracteristic being measured.

Distance and time are good examples.


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share by brand, customer characteristics and other

factors.

Aspects of market research include advertising and

promotional research, product research and

distribution.

Market research companies also sell a range of

services, and will frequently undertake research for

government, both national and local, academic

projects and not-for-profit organizations.

CONCLUSION

Obtaining and using data as information is an

important part of understanding and solving any

problem.

There is little doubt about the volume of data now

available, and any search of the Internet can easily

produce reams of computer printout.

As with all problem solving we need to work within

boundaries that ensure the problem remains

manageable and yet does not exclude new avenues of

enquiry.

Given the diversity of possible data sources we need

to check that data is appropriate, adequate andwithout bias.

As discussed, the choice is rarely between secondary

data (existing data) or primary data (new data that

needs to be collected for the specific purpose).

Secondary data will help describe and define the

existing problem.


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If the purpose of the statistical investigation requires

the collection of original data, then the sample survey

is probably the most widely used method in business

and economics.

The choice that you make will depend on the size of

the survey, the resources available and the

sophistication of the analysis necessary.

quantitative techniques in business case study

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