Program |
Diploma in Business |
Unit Number and Title |
Unit 6 Assignment on Business Decision Making copy |
QFC Level |
Level 5 |
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.
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;
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.
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 |
A questionnaire to carry out the survey to show the required data;
Task 2
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
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
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
l= 50, c.f-=58, f=17, h=10
Median = 50+ (60-58)/17*10
Median= 51.18
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
Mode = l+ (fm-fp)/2fm-fp-fs*10
Mode= 50+ (17-16)/ 34-16-16*10
Mode= 50+ ½*10
Mode= 55
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 |
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 |
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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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.
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.
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.
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;
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
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:
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.
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.
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