Unit 6 Assignment on Business Decision Making copy

Unit 6 Assignment Business Decision Making copy

Unit 6 Assignment on Business Decision Making copy

Program

Diploma in Business 

Unit Number and Title

Unit 6 Assignment on Business Decision Making copy

QFC Level

Level 5

Introduction

In this report, the requirements and the methods for the business decision making has been discussed. For the purpose of the decision making research has been conducted for the collection of relevant information. Primary and secondary data has been collected to know the perceptions of the customers regarding the taste, preferences and buying behavior in the coffee industry. The data is collected to help the business decision making for the launch of the new brand of coffee sachets in London.

The report also focuses on the analysis of the past years data regarding sales, cost and profit of the company with the help of various charts and graphs including the trend line. The forecast for future years are identified in sales, costs and profit are made and recommendations are made to the CEO of the company which helps the company in achieving the desired objectives of the company.

Task 1

Collection of data considering the primary and secondary data to gather information about the market dynamics.

The data collection should be based on the requirement to identify the taste, preference, consumer profile, attitude towards the brand, and buying behavior of the customers. The data collection should also contain information regarding the competitors’ products. Data can be of two types:-

Primary sources of data: Primary data is used for the analysis process for the business decision making. It is the first hand data which is collected for the need of the research conducted. The chances of risk in the relevance of the data are minimal as these data are collected from the target customers directly. In the process of the collection of the data primarily, the target group of the customers are identified to collect the relevant information regarding the research work (Swart, 2014).
In this case, the perception of the customers can be identified regarding the launch of the new brand in coffee sachet in London. The methods that can be used for the collection of the primary data are;

  • Target group: In the method of target group, a set of the target customers are developed and their perceptions can be known through the preparation of the questionnaires which may include such questions which provide the relevant information regarding the research. 
  • Interviews: Through interviewing the customers, their preferences, taste, choices among the coffee brands, their expectations from the coffee brands etc. can be recorded.
  • Schedule method: Through schedule method also, the perceptions of the customers can be known by appointing enumerations at the coffee outlets and they may ask the customers about their feedbacks on the coffee, their preferred brand and taste.
  • Secondary sources of data: Secondary data is the data which is collected in support of the primary data. The data can be collected through internet, published data in newspapers, magazines, company’s financial and sales reports.

The data which can be collected from the financial statements of the company can provide the information about the profit trends in the coffee market which can provide the base for the decision regarding the launch of the coffee sachets in the market. Through internet also, the new trends in the tastes of coffee or categories of the coffee prevailing in the market can be known. The information related to the current trend of the instant coffee can be obtained through the secondary data. Data in the newspaper publications can help to know the popular brands which are gaining popularity in the coffee industry and their new innovations so that the direct competitors can be identified.

Plan for collection of Data

Primary data to be collected

The data focusing on the taste, preference, attitude, choice of the brands among coffee industry shall be collected.

Secondary data to be collected

The data related to the market trends, competitor’s products, customers’ perception, buying behavior of customers, company’s sales trend etc. shall be collected.

Time period

The research work is needed to be completed within 80 days from the development of the research plan.

Tools for primary data collection

Questionnaires can be prepared to know the customers perceptions.

Tools for secondary data collection

Internet, business publications relevant to the launch of coffee sachets in London by a new brand.

Method of sampling frame

Random sampling and Stratified sampling

  • Survey methodology: It is the methodology to conduct a study of the data collected from the questionnaire in the survey data collection technique. The management can use this methodology for the collection of data from the primary source. The most important part in the survey methodology is the questionnaire that is used to collect the data from the target group of customers regarding their perceptions. In the given questionnaire, twelve questions will be included and shall be given to the targeted customers selected as the sample size in sampling.
  • Sampling method: It is the method in which a target group of the customers are selected from the population. There are different types of sampling methods available. The type of method to be undertaken depends on the need of the research we are conducting. The population is huge for the researcher to do survey of all. Thus, a carefully chosen small group of population can be used to represent the entire population.
  • Sampling frame: Sampling frame is the information used to determine the sample population for the collection of data. In this research, total population shall be divided into different size. These are known as target population for the research and shall use as target population. (West, 2016)From the different methods of the sampling frame we will use are;
  • Random sampling: In the random sampling everyone in the population has a chance of getting chosen. It is the easy and quick method.
  • Stratified sampling: In this method, the targeted group will be divided in smaller groups based on the criteria, such as age, gender, etc. The focus of this method is to make the sample as representative as possible. (Brickman Bhutta, 2012)

A questionnaire to carry out the survey to show the required data;

  • Age Bracket
  1. 18 years to 25 years
  2. 26 years to 35 years
  3. 36 years to 40 years
  4. 41 years to 45 years
  • Specify your gender
  1. Male
  2. Female
  • Are you working?
  1. Yes
  2. No
  • Do you drink coffee?
  1. Yes
  2. No
  • How often is your coffee intake?
  1. 0 to 2 cups
  2. 3 to 5 cups
  3. 5 to 7 cups
  4. 7 to 9 cups
  • Which is your preferred brand of coffee?
  1. Nescafe
  2. Costa Coffee
  3. Starbucks
  4. Baristas
  • What is your preference in coffee type?
  1. Ground coffee
  2. Instant coffee
  • What taste do you prefer?
  1. Caramel
  2. Cappuccino
  3. Strong coffee
  4. Light coffee
  • What packaging of coffee do you prefer?
  1. Bulk coffee
  2. Pillow pack coffee
  3. Coffee sachets
  4. Single serve coffee
  • Would you prefer sugar free coffee?
  1. Bulk coffee
  2. Pillow pack coffee
  3. Coffee sachets
  4. Single serve coffee
  • What offers attract you to the new coffee brands?
  1. Discounted price
  2. Free gifts
  3. Buy one get one
  4. Extra quantity at reasonable price
  • What make you loyal towards the brand?
  1. Quality of coffee
  2. Quantity offered
  3. Taste of coffee
  4. Attractive packaging

Task 2

Understand a range of techniques to analysis data effectively for business purpose

Amount (£) spent

No. of orders

0.5-10

7

10-20

9

20-30

12

30-40

14

40-50

16

50-60

17

60-70

16

70-80

15

80-90

8

90-100

6

Ans.

For further analysis of data following tools and techniques are used

  • Mean: Mean is also known as the arithmetic mean. It is the average of the numbers by adding all the numbers and the dividing it by the sum of total count of numbers. It is mainly the model of our data set. (Tegzes, 2015)

Amount (£)

Mid value(x)

No of orders (f)

fx

0.5-10

5.25

7

36.75

10-20

15

9

135

20-30

25

12

300

30-40

35

14

490

40-50

45

16

720

50-60

55

17

935

60-70

65

16

1040

70-80

75

15

1125

80-90

85

8

680

90-100

95

6

570

Total

 

120

6031.75

Mean = ∑fx/∑f
Mean = 6031/120
Mean= 50.26

  • Analysis: As per the given table mean are 50.26 this shows that the average no of orders are 50.26.
  • Median: Median represents the middle number. For calculation of median the numbers should be arranged from lowest to highest and then the middle number is calculated.

Amount (£)

No of orders (f)

Cumulative frequency

0.5-10

7

7

10-20

9

16

20-30

12

28

30-40

14

42

40-50

16

58

50-60

17

75

60-70

16

91

70-80

15

106

80-90

8

114

90-100

6

120

Here N is 120 and N/2= 120/2= 60
The cumulative frequency just more than 60 is 75
The median class is 50-60

formula

l= 50, c.f-=58, f=17, h=10
Median = 50+ (60-58)/17*10
Median= 51.18

  • Analysis: As per the given table median is 51.18 which represent that the middle number of the orders that means 50% of the orders are below 51.18 and 50% of the orders are above 51.18.
  • Mode: It is a value which occurs most frequently in a data set.

Amount (£)

No of orders (f)

Cumulative frequency

0.5-10

7

7

10-20

9

16

20-30

12

28

30-40

14

42

40-50

16

58

50-60

17

75

60-70

16

91

70-80

15

106

80-90

8

114

90-100

6

120

Highest frequency is 17 so the mode class is 50-60

fm= 17, fp=16, fs=16, l=50

formula2

Mode = l+ (fm-fp)/2fm-fp-fs*10
Mode= 50+ (17-16)/ 34-16-16*10
Mode= 50+ ½*10
Mode= 55

  • Analysis: as per the given table the mode is 55 this represents that no of orders which repeats the most is 55.
Calculation of the following:

1. Range: Range is the simplest measure of variation. It is basically the difference between the largest and the smallest observation in a data set.

Amount (£)

Range (Upper value-lower value)

0.5-10

9.5

10-20

10

20-30

10

30-40

10

40-50

10

50-60

10

60-70

10

70-80

10

80-90

10

90-100

10

2. Standard deviation: It is a measure to identify the dispersion of a set of data from its mean. (Liu, 2015)

Amount (£)

No of orders (f)

Mid value(x)

xbar

x-xbar

(x-xbar)2

f(x-xbar)2

0.5-10

7

5.25

50.26

-45.01

2025.9

14181.3

10-20

9

15

50.26

-35.26

1243.268

11189.41

20-30

12

25

50.26

-25.26

638.0676

7656.811

30-40

14

35

50.26

-15.26

232.8676

3260.146

40-50

16

45

50.26

-5.26

27.6676

442.6816

50-60

17

55

50.26

4.74

22.4676

381.9492

60-70

16

65

50.26

14.74

217.2676

3476.282

70-80

15

75

50.26

24.74

612.0676

9181.014

80-90

8

85

50.26

34.74

1206.868

9654.941

90-100

6

95

50.26

44.74

2001.668

12010.01

 

∑f=120

       

71434.54

formula3
Standard deviation = √71434.54/120
Standard deviation= √595.29
Standard deviation= 24.40

3. Lower quartile (25th percentile)
The value of the middle number between the first term and median is known as lower quartile. It is denoted as Q1.

Amount (£)

No of orders (f)

Cumulative frequency

0.5-10

7

7

10-20

9

16

20-30

12

28

30-40

14

42

40-50

16

58

50-60

17

75

60-70

16

91

70-80

15

106

80-90

8

114

90-100

6

120

Q1=   (n+1/4)*thterm
Q1=  120+1/4
Q1= 30.25
So the lower quartile is 14

4. Upper quartile: The value of the middle number between the median and last term is known as upper quartile. (Hytönen, 2013) It is denoted as Q3.

Amount (£)

No of orders (f)

Cumulative frequency

0.5-10

7

7

10-20

9

16

20-30

12

28

30-40

14

42

40-50

16

58

50-60

17

75

60-70

16

91

70-80

15

106

80-90

8

114

90-100

6

120

Q3=  3(n+1/4)*thterm
Q3=  (120+1/4)
Q3= 90.75
So the upper quartile is 16

5. Inter quartile range: The value of middle number between upper quartile and lower quartile.
IQR= Q3- Q1
IQR= 16-14
IQR= 2

6. Correlation coefficient: It is a number which shows the relationship between two or more variables.

Sales (x)

Temperature (y)

(xy)

x2

y2

20

320

6400

400

102400

24

411

9864

576

168921

11

192

2112

121

36864

17

259

4403

289

67081

9

170

1530

81

28900

15

243

3645

225

59049

25

430

10750

625

184900

121

2025

38704

2317

648115

formula4

r = 7*38704-121*2025/√ (7*2317-1212) (7*648115-20252)
r =270928-245025/√ (16219-14641) (4536805-4100625)
r = 25903/√1578*436180
r= 25903/√688292040
r= 25903/26235.32
r= 0.98

The Correlation coefficient represents the relationship between sales and temperature. 0.98, correlation coefficient shows that sales and temperature are highly influenced by each other. As the temperature will increase the sales will also increase and vice versa.
Quartiles and correlation coefficient are helpful for a business because there system analysis helps in process improvement as well they provide summary statistics to analysis the data variability this play a vital role in benchmarking purpose.

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Task 3

A presentation is prepared on the sales, costs and profit (including the trend lines) with recommendation for CEO of the company.

business decision making ppt 1

business decision making ppt 2

business decision making ppt 3

business decision making ppt 4

business decision making ppt 5

business decision making ppt 6

business decision making ppt 7

business decision making ppt 8

business decision making ppt 9

Provide information in an appropriate format for decision making in an organizational context.

Bar chart for sales

sale 1

  • Analysis: A bar chart of sales represents the comparison in sales in the given years. It shows that the lowest sales were recorded in the year 2000 and the highest sales were recorded in the year 2005. Since, the year 2005, the sales are not growing in the consistent manner.
Bar chart for cost

all costs 1

  • Analysis:  A bar chart of all costs including the direct costs and indirect costs represents the costs incurred in the given years. Lowest costs were incurred in the year 2001 and the highest costs were incurred in the year 2008. It should be considered by the management to efficiently use the resources to minimize the costs.
Bar chart for profit

profit1

  • Analysis: A bar chart for profit is prepared to represent the profits gained by the company in the given years. The year in which the company gained the lowest profit was 2002 and the highest profit was gained by the company in the year 2005. The financial statements of the year 2005 should be considered to find out the policies adopted by the company that year to gain more profits in the future. The trend of profits in these years are very fluctuating that should be avoided and necessary steps should be taken by the company to make the profit increase on a continuous basis.
Line chart for sales

sale2

The trend line is used to forecast the sales of the future years. The sales volume of the past years is used to forecast the future sales volume. In the given situation, the sales will be growing in between £3,00,000 to £3,50,000 for the years 2011 to 2013.

Line chart for costs

all costs2

The costs will be forecasted for the future years from 2011 to 2013. With the given trend line analysis, it can be said that the costs are growing gradually. In the three years the cost will increase from £1,60,000 to £1,80,000.

Line chart for Profit

profit 2
In this scenario, the profit trend is increasing. The profit forecast for the future years 2011 to 2013 will be from £1,40,000 to £1,60,000.

Business Report for Regional Manager

To

The Management

Sub- Providing explanation on the relationship between sales, costs and profit and benefits of forecasting.

In the growth of the business of the company, three elements are the most important to make the organization capable in achieving the goals and objectives of the company. The growth in the sales is the outcome of the strong market position of the company and should be always growing. The costs including the direct and indirect costs which occur in the projects undertaken by the companies must be minimized with the help of the effective resources employed by the company. These lower costs depict the efficient use of resources undertaken by the management. The trend of the profits also shows the financial position of the company.

There is a strong relationship between these three elements i.e. sales, cost and profit. When the costs incurred on the business projects are cut down and the volume of sales increases then this ultimately results in high profit generation. This scenario is helpful in the development of the growth of the company. The relationship of the costs incurred and sales achieved helps the organizations to determine the selling prices of the products.

Forecasting is based on the organization and the information required for the research. Some of the benefits of forecasting are;

  • Forecasting will help to keep an eye on the upcoming trends in the market which will make your company aware of the prospective competitors in the market.
  • It will help to predict the demands of the customers which will help to develop products according to the needs of the customers.
  • For implementing a new business plan it is important to forecast everything required to make the plan implementation successful.

Hence, we can conclude that their relationship affects the business health and growth. Forecasting the new challenges can help to grow sales and profits. The manager should take steps to forecast accurately focusing on the relationship of sales, cost and profits.

From
Management Consultant
Grahams Consultants Limited

Task 4

Description of Activity

Activity

Preceding-Activity

Period

Preparation

[A]

-

6days

Business Planning

[B]

  [A]

4days

Recruitment and selection

[C]

[A]

38days

Installation of peripherals

[D]

[B]

17days

Initial training

[E]

[D]

6days

Design

[F]

[E]

11days

Conversion

[G]

[F]

11days

Development of norms

[H]

[C]

4days

Assessment

[I]

[B]

12days

Continuous testing

[J]

[D]

11days

Policy documentation

[K]

[G,H,I,J]

22days

Appraisal

[L]

[K]

22days

 

Following is the network diagram:

Unit 6 Assignment Business Decision Making copy

 

  • Project duration:=6+4+38+17+6+11+11+4+12+11+22+22 =164 days
  • Critical path: Critical path is the longest path which is followed to complete a project undergoing every activity. Critical path analysis helps the manager to manage the time as well as flow of the work. (Hendriks, 2016) Critical path in the above project is:  = A®B®D®E®F®G®K®L = 99 Days

Task 5:

Year

Project Super

Project Sonic

0

-400000

-400000

1

55000

318000

2

100000

20000

3

110000

20000

4

95000

6000

5

40000

50000

NPV and IRR of both the projects using spreadsheet method and assuming the rate at 10% is as follows: (Robison, 2015)

 

Project Super

Project Sonic

NPV

(£86,352.38)

(£40,190.66)

IRR

0%

2%

Report to the Board members

It can be seen that NPV of both the projects is negative hence both the projects are not profitable but if any project is to be selected between both then project Sonic should be chose as its having higher NPV then project Super and both the projects have lesser IRR so both are not profitable but if any project is to be selected on the basis of IRR then project Sonic should be selected as its having higher IRR than project Super.
It is recommended that project Sonic should be selected since it has higher NPV and IRR as compared to project Super.

Conclusion

It can be concluded that the data collected from the primary and secondary sources can be used to identify the current taste, preference and the market position of the company. And, accordingly the decision regarding the launch of new brand of coffee sachet in London can be taken. A critical path as presented in the report will help the operation management to manage the time and flow of work accordingly. The past data in sales, costs and profit trends can help the company to forecast future trends in the respective areas to fulfill the objectives of the company.

References

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Ghanbarian, B., Torres-Verdín, C. & Skaggs, T.H. 2016, "Quantifying tight-gas sandstone permeability via critical path analysis", Advances in Water Resources, vol. 92, pp. 316-322.
Hendriks, M., Verriet, J., Basten, T., Theelen, B., Brassé, M. & Somers, L. 2016, "Analyzing execution traces: critical-path analysis and distance analysis", International Journal on Software Tools for Technology Transfer, .
Hytönen, T.P., Lacey, M.T. & Parissis, I. 2013, "The vector valued quartile operator",Collectanea Mathematica, vol. 64, no. 3, pp. 427-454.
Liu, C., Liu, C., Lin, T. & Chen, L. 2015, "A satellite-derived typhoon intensity index using a deviation angle technique", International Journal of Remote Sensing, vol. 36, no. 4, pp. 1216.
March, S., Iskenius, M., Hardt, J. & Swart, E. 2013, "Methodological considerations for data linkage of primary and secondary data in occupational epidemiology studies",Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, vol. 56, no. 4, pp. 571.
Othman, N.T.A., Misnon, R., Abdullah, S.R.S., Kofli, N.T., Kamarudin, S.K. &Mohamad, A.B. 2011, "Assessment of Programme Outcomes Through Exit Survey of Chemical/Biochemical Engineering Students", Procedia - Social and Behavioral Sciences,vol. 18, pp. 39-48.
Robison, L.J., Barry, P.J. & Myers, R.J. 2015, "Consistent IRR and NPV rankings",Agricultural Finance Review, vol. 75, no. 4, pp. 499-513.
Saffy, H.A., Northrop, W.F., Kittelson, D.B. &Boies, A.M. 2015, "Energy, carbon dioxide and water use implications of hydrous ethanol production", Energy Conversion and Management,vol. 105, pp. 900-907.
Swart, E., Stallmann, C., Powietzka, J. & March, S. 2014, "Data linkage of primary and secondary data: a gain for small-area health-care analysis?", Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, vol. 57, no. 2, pp. 180.
Tegzes, A.D., Jansen, E. & Telford, R.J. 2015, "Which is the better proxy for paleo?current strength: Sortable?silt mean size ( SS¯) or sortable?silt mean grain diameter (dSS)? A case study from the Nordic Seas", Geochemistry, Geophysics, Geosystems, vol. 16, no. 10, pp. 3456-3471.
West, P.W. 2016, "Simple random sampling of individual items in the absence of a sampling frame that lists the individuals", New Zealand Journal of Forestry Science, vol. 46, no. 1, pp. 1-7.