Awarding Body:

Course: HND Diploma in Business

Name of the Subject: Business Decision Making.

Assessor:

Submitted By (Student Name):

Student ID:

Number of words: 2500

Regent College

Higher Education

1 Table of Contents

2 Introduction........................................................................................................................1

3 Task 01...............................................................................................................................2

3.1 Plan for the gather ion of primary and secondary data for a given business problem 2

3.2 Survey methodology and sampling frame...................................................................3

3.3 Questionnaire...............................................................................................................4

3.4 Information for decision making and analysis of results and data..............................5

3.4.1 Mean, mode, median and quartiles......................................................................5

3.4.2 Importance of the above calculations and the recommendations.........................7

3.4.3 The range, inter quartile range and the standard deviation..................................8

3.5 How quartiles, percentiles and the correlation coefficient are used to draw useful conclusions in a business context...........................................................................................9

4 Task 02.............................................................................................................................11

4.1 Graphs, conclusions and Business presentation........................................................11

4.2 Business report..........................................................................................................14

4.3 Information processing tools.....................................................................................15

4.4 Project plan and critical path.....................................................................................16

4.5 Financial tools for decision making..........................................................................18

5 Conclusion........................................................................................................................20

6 References........................................................................................................................21

7 Bibliography.....................................................................................................................22

i

2 Introduction

In the modern business era, successfulness of the companies importantly rely on number of

decision making procedures undertaken by their directors, managers, etc. Timely, effective

decisions enable companies to face the changes in the internal and external business

environments, identifying the market trends and changing customer requirements allowing

the companies to achieve a competitive edge in the market. Under this assignment the

decision making process and the options used by the companies will be identified in detail

paying attention to the market research methodologies and options of obtaining data. The

effective usage of Gantt charts and Network diagrams will be discussed presenting the

number of data representation options like the graphs and charts.

1

3 Task 01

3.1 Plan for the gather ion of primary and secondary data for a given business

problem

As per Kothari (2009, p 95) usage of a proper data gather ion method is very much vital for

the success of any new business. A data gather ion method should be convenient for the

researcher as well as to the sources from which the data will be gathered. In obtaining the

required for the research both the primary and secondary data can be used properly which

will allow the researcher to obtain a large variety of data that will guide him in identifying the

contemporary issues prevailing the Restaurant in London.

In gather ing the data that are required to carry out the required market survey, Restaurant in

London will have to select a suitable sampling methodology which will have to be used in

gather ing the required data. As per Bethlehem (2009, p 106) a sampling method is required

as the organisation will not be in a position to gather the data from all the customers in their

niche market. So they will have to select a group or a set of individuals from which they can

obtain the required data which will represent all its customers.

2

3.2 Survey methodology and sampling frame

In gather ing the data that are required to carry out the required market survey, Restaurant in

London will have to select a suitable sampling methodology which will have to be used in

gather ing the required data. As per Bethlehem (2009, p 106) a sampling method is required

as the organisation will not be in a position to gather the data from all the customers in their

niche market. So they will have to select a group or a set of individuals from which they can

obtain the required data which will represent all its customers. Random sampling is where

each and every member in the target market has an equal probability of getting selected for

the sample. Stratified sampling is where the entire population will be divided into number of

groups and then the individuals will be selected using the random sampling method. In this

situation Restaurant in London can properly use the simple random sampling method given

the market is a niche market. As the market is not a big one the simple random sampling

method will allow Restaurant in London to conveniently gather the data required for the

market survey. Survey methodologies will be used in order to gather the data from the

selected sample. The number of survey methodologies will be face to face conversations,

telephone conversations, online surveys and etc. In this situation Restaurant in London can

properly use a questionnaire to gather the required data as the market is a niche market. That

will allow Restaurant in London to take a friendly approach towards customers getting closer

to them.

3

3.3 Questionnaire

In carrying out the required market research it will be very much vital for Restaurant in

London to obtain a variety of information from the market and customers that will enable

them to identify the market position properly. Identification of the exact market position will

enable Restaurant in London to develop strategies to expand their market across London. In

obtaining the required information Restaurant in London can use number of data gather ion

methodologies but using a properly prepared questionnaire will enable them to properly

obtain the required data. As explained by Gillham (2008, p 88) a questionnaire for Restaurant

in London which can be used in gathering the data from the customers can be developed as

follows.

Questionnaire

1 What are the number of types of restaurants you visit and what is the most preferred

restaurant type?

2 What is the reason for you prefer that the specific type of restaurant?

3 Are you satisfied about the value you get from the sauce when compared with the price

you pay?

4 What is the source from which you got to know about the above restaurant?

5 Are you satisfied about the availability of the above mentioned restaurant?

6 What are the further that should be made into the specific restaurant?

7 What are the times you usually visit a restaurant?

8 How often you visit a restaurant?

9 What is your monthly income level? {Low / medium / high}

4

3.4 Information for decision making and analysis of results and data

3.4.1 Mean, mode, median and quartiles

The given data set should be arranged in the ascending order before calculating the mean,

mode, median and quartiles which can be showd as follows.

3, 4, 4, 5, 5, 6, 6, 7, 7, 7, 7, 7, 7, 8, 8, 9, 10 , 16, 33

The mean

As explained by Urdan (2005, p 33) the mean will be the average of a given data set. The

mean for the above data set can be calculated as follows.

Mean = [x1+x2+x3+…… xn] /n

Mean = [3+4+4+5+5+6+6+7+7+7+7+7+7+8+8+9+10+16+33] /19

= 8.368

The mode

The mode can be identified as the most repeated element of a given data set. The most

repeated element in the above data set is 7 where it will be the mode of the above data set.

The median

As per Rubin (2012, p 59) the middle value of a given data set will be interpreted as the

median of that data set. The median of the given data set will be calculated as follows.

If the numbers in the list are “n”, then the Median would be,

Median = [n+1] /2 th element.

Median = [19+1] /2 (as the numbers in the above data set is 19)

= 10th element

= 7

The quartiles

5

As per Griffiths (2008, p 92) the quartiles will show the dispersion of a given data set where

the quartiles will divide the given data set into three equal parts. Three quartiles will be

calculated for a given data set namely the lower quartile, middle quartile and the upper

quartile. The middle quartile of a given data set will be the same as the median of the data set.

So the middle quartile in this situation will be 7 which is the median. The lower and the upper

quartiles will be calculated as follows.

Lower Quartile = [n +1] * ¼

= [19+1] * ¼ th element

= 5th element

= 5

Upper Quartile = [n+1] * ¾

= [19+1] * ¾ th element

= 15th element

= 8

6

3.4.2 Importance of the above calculations and the recommendations

As explained by Black (2011, p 520) in this scenario the average number of customers visited

to the restaurant can be identified as 8.368 where the restaurant in London can identify the

most probable number of customers that will come per month. The most repeated number of

customers placed per week can be identified as 7 supporting the above average situation. And

even the median of the above data set is 7 which further supports the above conclusion. So it

will be important to the restaurant in London to be ready to cater to at least 7 or 8 number of

customers per week being equipped with the relevant human and other resources. But there

will be deviations from this average situation which is clearly showed through the above data

set. So the restaurant in London should be ready with the required resources to cater to such

exceptional situations which can be due to a seasonal fluctuation or another market or

environmental situation. But anyway developing a more customer friendly and a convenient

number of services system will enable the restaurant in London to attract more and more

customers through their website increasing the number of number of customers received per

month.

7

3.4.3 The range, inter quartile range and the standard deviation

The range

As per Howell (2008, p 79) the range of a given data set can be identified as the difference

between the highest and the lowest values of a given data set. The range of the above data set

can be calculated as follows.

The range = Highest value – lowest value

= 33 – 3

= 30

Inter quartile range

As per Sharma (2007, p 138) the difference between the upper and lower quartiles will be

showed through the inter quartile range. The calculation of the upper and lower quartiles will

be as above. Using the above calculations the inter quartile range of the above data set can be

calculated as follows.

Inter quartile range = [Q3 - Q1]

Inter quartile range = [8 – 5]

= 3

The standard deviation

As explained by Urdan (2005, p 33) the standard deviation will show the deviation of the data

in a given data set from the mean of that data set. This will simply be a measure of dispersion

identifying the average of the elements in the given data set. The standers deviation of the

above data set will be calculated as follows.

Standard deviation = √ ∑k=0

n

(x−mean )2 / n

Standard deviation = 6.4089

8

3.5 How quartiles, percentiles and the correlation coefficient are used to draw useful

conclusions in a business context

The quartiles and the percentiles can be identified as the mathematical options that are used

to identify the dispersion of a given data set. As per Hardman (2009, p 99) the quartiles will

divide a given data set into three equal parts allowing the identification of the dispersion. In

the above scenario, the lower quartile is 5 indicating that 25% of the elements in the given

data set are equal or lower than 5 in value. The middle quartile which is the same as the

median is equal to 7 indicating that 50% of the elements in the above data set are equal or

lower than 7 in value. The upper quartile is 8 indicating that 75% of the elements in the above

data set are equal or lower than 8 in value. These data can be properly used by the restaurant

in London in identifying the most likely situation of the number of customers per week and to

identify the changes in the number of customers along with time and value. So in the above

scenario the number of number of customers received per week lies around the values of 7

and 8 which shows the most likely scenario that the restaurant in London will have to tackle.

The percentiles will divide a given data set into 100 equal parts and the interpretation of the

percentiles will be the same as that of quartiles. The calculation of the percentiles will be

useful if there is a large number of elements in the given data set.

The importance of mean as the best measure

As per Kumar (2006, p 145) the mean of a given data set will show the average of a given

data set which is more convenient and easy to be understood by the users. In the above

situation the average number of number of customers received per week can be identified as

8 where the restaurant in London should be ready to cater to at least 8 number of customers

per week. In that light the organisation will have to be equipped with the relevant resources

including the material and the human resources in order to deliver at least 8 number of

customers per week. The upper quartile of the given data set is also 8 which is same as the

mean indicating that 75% of the times the number of number of customers received per week

is equal or lower than 8 insisting the importance of the mean as the best measure in this

scenario. However 25% of the values in the data set is higher than 8 which are the extreme

situations deviating from the mean. So it will be advisable to the restaurant in London to be

ready to tackle such extreme situations without losing the customers. So the mean can be

used as the best measure in generating business decisions with regard to the given situation.

Pearson’s coefficient of skewness

9

As explained by Munro (2005, p 46) the Pearson’s coefficient of skewness will show the

skewness of a given data set as a numerical value. This importantly handles with the

graphical representation of the data set through a symmetry where in the situations of the

mean, mode and the median are not similar the data symmetry will be positively or negatively

skewed based on the values of the above measurements. The Pearon’s coefficient of

skewness for the above data set can be calculated as follows.

Pearsons coefficient of skewness = 3* [mean – median] / Standard deviation.

= 3*[ 8.368 – 7] / 6.4089

= 0.6404

The coefficient of 0.6404 shows that the above data set is positively skewed indicating the

number of number of customers received per week lie below the mean of the given data set.

10

4 Task 02

4.1 Graphs, conclusions and Business presentation

To present we can use Microsoft PowerPoint in each slide containing following number of details.

Data representation through bar graphs

Table 1 Data

of

organisational performance

Sales, marketing expenses, profits during the last eight years

11

1

2

3

4

5

6

7

8

0 100 200 300 400 500 600 700 800 900

profitsmarketingsales

Year Sales Marketing profits

1 375 45 145

2 495 65 160

3 590 88 242

4 645 99 218

5 495 50 180

6 575 78 220

7 665 97 250

8 810 118 290

Data representation through pie charts

18% 2

11%

313%

414%5

11%

612%

714%

817%

Sales

Sales - each year - as a percentage of the total sales

19% 2

9%

314%

413%5

11%

613%

715%

817%

Profits

Profits - each year - as a percentage of the total profit

12

17% 2

10%

314%

415%5

8%

612%

715%

818%

Marketing

Marketing expenses in each year as a percentage of the total marketing expense

Generating the trend equation through a scatter diagram

0 1 2 3 4 5 6 7 8 90

100

200

300

400

500

600

700

800

900

f(x) = 44.0476190476191 x + 383.035714285714R² = 0.67380337147779

salesLinear (sales)

year

sale

s rev

enue

Chart 5 Trend Analysis through the Scatter Diagram

Trend Line equation y = 44.048x + 383.04

13

4.2 Business report

As per Brechner (2008, p 553) when analysing the above data it is clearly notable that there is

a trend in the sales figures, marketing figures and even in the profit figures over the eight year

period. There is a linear relationship between the sales and the marketing expenses. As the

marketing expenses have been increased gradually the sales figures have increased indicating

that the organisation is operating in a competitive environment where the market share of the

other players can be gathered through effective marketing and product number ofiation. The

increase in the marketing expenses will allow the organisation to carry out more effective

marketing campaigns being closer to the customers. That will attract more and more

customers towards the organisational products leading the sales figures to increase given the

linear relationship. The finance department should keep on investing in the marketing

activities rather than reducing the marketing budget which will lead to a loss in market share.

So the organisation should keep on developing new products and launching them to the

market while retaining the existing market share through continuous investments in the

marketing budget. That will finally result in the overall profits of the organisation to increase.

14

4.3 Information processing tools

As explained by Kaid (2004, p 413) the information processing systems will include the

processes, rules and guidelines in order to process data into meaningful information. When

Computing devices are used in such processes that will enable more effective, accurate and

efficient data processing allowing the organisation to make effective and quick decisions. The

number of levels within the organisation will be using number of information processing

systems in order to make number of business decisions. The strategic level will use the

decision support systems and management information systems to analyse the data in

developing strategic decisions. The tactical level will be using management information

systems in order to make short to medium term business decisions. The operational level will

use the transaction processing systems and information processing systems in order to make

decisions regarding the day to day business operations. The usage of these information

systems will allow the organisation to obtain more effective, efficient and reliable

information which can be used for effective decision making.

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4.4 Project plan and critical path

Operational Outcomes

Following will be the way in which data represent in the network diagram.

ID of the activity Time period (Weeks)

Earliest Starting time Latest Starting time

Data representation in the Network Diagram

The Network Diagram

16

Activity Duration

(Weeks)

Preceding

activity

A Preliminary

design

5 -

B Materials research 3 -

C Obtain material

quotas

2 A

D Draw up plans 5 A

E Marketing research 3 A

F Costing 2 C

G Get planning permissions 4 D

H Design and

research

6 B, E

I Pricing estimates 2 H

J Final report 6 F, G, I

Gantt chart

17

4.5 Financial tools for decision making

The payback period

As explained by Morrell (2007, p 163) this calculation will identify the time consumed to

repay an investment which can be calculated as follows for the given data set.

Table 3 of the payback period

The payback period = 3 years + 60000/100000 years

= 3.6 years

Calculation of the NPV

As per Lasher (2007 p, 436) the NPV calculation will show the present value of the future

cash flows discounted using the cost of capital rate of the organisation.

Year Net Cash Flow Discounting factor Present value

Y0 (500 000) 1 (500 000)

Y1 200 000 0.909 181800

Y2 120 000 0.826 99120

Y3 120 000 0.751 90120

Y4 100 000 0.683 68300

Y5 60 000 0.621 37260

The PV calculations

NPV for the project = (500000) + 181800 + 99120 +90120 + 68300 + 37260

= (23400)

Calculation of ARR

18

Year Net Cash

Flow

Cumulative Net Cash

Flow

Y0 (500 000) (500 000)

Y1 200 000 (300 000)

Y2 120 000 (180 000)

Y3 120 000 (60 000)

Y4 100 000

Y5 60 000

As per Brabner (2007, p 6) in the below calculation it will show the average annual profit

before interest and tax as a percentage of the total initial investment which can be calculated

as follows.

The profit calculations

ARR = (Average profit before interest and tax / Initial investment) * 100 %

= [(100000+20000+20000-40000) / 5] / 500000] * 100 %

= 4%

19

Year Net Cash Flow Depreciation Profit

Y1 200 000 100 000 100 000

Y2 120 000 100 000 20 000

Y3 120 000 100 000 20 000

Y4 100 000 100 000 0

Y5 60 000 100 000 (40 000)

5 Conclusion

In the modern business era success of a company will depend on how the decision making

will carry out number of parties in the organisation. In the process of taking number of

decisions regarding business activities it is important to use proper information and data

analysis options including proper data gather ion methodologies. Through using information

systems will enable and increase the accuracy of data processing which will then facilitate the

reliable decision making. Companies can largely use the number of charts and graphs in

presenting the information gathered. The network diagrams and the Gantt charts can be

properly used in managing number of business projects. In assessing investments the

companies can use the options like NPV, ARR and payback period. But the final investment

decision should be based on the NPV value.

20

6 References

Bethlehem, J. (2009), Applied Survey Options: A Statistical Perspective, John Wiley & Sons,

p 106

Black, K. (2011), Business Statistics: For Contemporary Decision Making, 7th Edition, John

Wiley & Sons, p 520

Brabner, A. (2007), Corporate Finance - Assignment One, GRIN Verlag, p 6

Brechner, R. (2008), Contemporary Mathematics for Business and Consumers, 5th Edition,

Cengage Learning, p 553

Chary, (2004), Production and Operations Management, 3rd edition, Tata McGraw-Hill

Education, p 8

Gillham, B. (2008), Developing a Questionnaire, 2nd Edition, Bloomsbury, p 88

Griffiths, D. (2008), Head First Statistics, Illustrated, O'Reilly Germany, p 92

Hardman, D. (2009), Judgment and Decision Making: Psychological Perspectives,

Illustrated, John Wiley & Sons, p 99

Howell, D.C. (2008), Fundamental Statistics for the Behavioral Sciences, 6th Edition,

Cengage Learning, p 79

Jain, K. (2007), Financial Management, 5th Edition, Tata McGraw-Hill Education, p 41

Kaid, L.L. (2004), Handbook of Political Communication Research, Illustrated, Routledge, p

413

Kothari, C.R. (2009), Research Methodology: Options and Options, 2nd Edition, New

Age International, p 95

Kumar, D. (2006), Six Sigma Best Practices: A Guide to Business Process Excellence for

Diverse Industries, Illustrated, J. Ross Publishing, p 145

Lasher, W. (2007), Practical Financial Management, Cengage Learning, p 436

Morrell, P.S. (2007), Airline Finance, 3rd Edition, Ashgate Publishing, Ltd, p 163

Munro, B.H. (2005), Statistical Options for Health Care Research, 5th Edition, Lippincott

Williams & Wilkins, p 46

21

Nykiel, R.A. (2007), Handbook of Marketing Research Methodologies for Hospitality and

Tourism, Illustrated, Routledge, p 25

Olson, J.E. (2003), Data Quality: The Accuracy Dimension, Illustrated, Morgan Kaufmann, p

145

Oppenheim, A.N. (2000), Questionnaire Design, Interviewing and Attitude Measurement, 2nd

Edition, Continuum International Publishing Group, p 102

Rubin, A. (2012), Statistics for Evidence-Based Practice and Evaluation, 3rd Edition,

Cengage Learning, p 59

Schwalbe, K. (2010), Information Technology Project Management, 6th Edition, Cengage

Learning, p 218

Sharma, J.K. (2007), Business Statistics, 2nd Edition, Pearson Education India, p 138

Urdan, T.C. (2005), Statistics in Plain English, 2nd Edition, Routledge, p 33

Urdan, T.C. (2005), Statistics in Plain English, Illustrated, Routledge, p 33

7 Bibliography

Hirschey, M. (2008), Fundamentals of Managerial Economics [With Access Code], 9th

Edition, Cengage Learning.

McNabb, D.E. (2010), Research Options for Political Science: Quantitative and Qualitative

Approaches, 2nd Edition, M.E. Shape.

Partin, R.L. (2009), The Classroom Teacher's Survival Guide: Practical Strategies,

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Sadagopan, S. (2004), Management Information Systems, PHI Learning Pvt. Ltd.

Woracek, J.A. (2008), Assessing the Validity of the English Language Development Assessment (ELDA), ProQuest.

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