Unit 6 Business Decision Making for Restaurant Assignment

Unit 6 Business Decision Making for Restaurant Assignment

Introduction

This Unit 6 Business Decision Making for Restaurant Assignment discusses the evaluation of data collected from the market sources for the making of decisions for a business containing the plan for collection of primary and secondary data related to the customer preferences and choices for opening a restaurant in London by an existing restaurant chain. In order to obtain the data questionnaire designed is also included. The analysis of data is also being made using quantitative tools and representation values along with charts and graphs. The project plan is also being made in this report by defining the critical path and analysing the data using quartiles, percentiles etc. A formal business report addressing the Board of Directors is also included to provide them relevant information using financial resources and tools such as discounted cash flow, net present value and IRR and evaluate that whether the project is financially viable or not.

Task 1

Report on Data Collection and analysis

Plan for data collection

• Primary and secondary sources of data: The primary data collection method will include collecting data from direct interaction with sources. Primary data is the data which is used first for the preparation of report and majorly for the analysis. The sources to be used for collection of primary data related to consumer preferences are questionnaires, interview and observation. The secondary sources of data include those sources which provide the information which is previously obtained by them from other sources. The secondary data is used to provide support to the primary data for the decision making process. The sources of collection of secondary data will include internet, newspapers, magazine publications, government notifications and press releases, business reports etc. (Schoenduwe, 2015)
• Survey methodology: The survey methodology is the process that will be used for the collection of primary and secondary data related to the preference of customers in context of food choices and taste in London. The data will be collected through questionnaire containing open ended and closed questions given to the selected sample of target population. Apart from this interviews will also be conducted from some of the target population so that the direct and spontaneous responses may be received from them. The survey method will also include collecting secondary data from internet and magazines to obtain information about customer behaviour in the hotel and restaurant industry in London. (Leeuw, 2013)
• Sampling Frame: Sampling frame refers to the list of all those categories which are included in the target population for conducting the survey into the subject matter in order to make decisions for the business. The sampling frame for conducting survey about the customer preference for restaurants in London through questionnaires and direct interviews shall include the target population of both working and non-working persons who often visit the restaurants including age groups of 18-25 years, 26-40 years and above 40 years. It will include not only tourists and visitors but also local residents of London.

Sample questionnaire

1. Select your age bracket from the following:

1. 0-18 years
2. 18-25 years
3. 26-40 years
4. 40-60 years
5. Above 60 years

2. What is your gender?

1. Male
2. Female

3. Are you a tourist/visitor or local resident of London?

1. Tourist
2. Visitor
3. Local resident

4. What is your employment status?

1. Government employee
2. Private employee
3. Self-employed/ business
4. Unemployed
5. Not working

5. Select your income bracket from the following:

1. Up to £ 2000
2. £ 2001 - £ 5000
3. £ 5001 - £ 10000
4. £ More than £ 10000
5. No Income

6. What do you prefer the most while deciding about a restaurant?

1. Food
2. Ambiance
3. Service
4. Cost
5. Brand
6. Others

7. Which type of food do you prefer?

1. Vegetarian food
2. Non-vegetarian food
3. Any of the above

8. How often do you go to a restaurant in a month?

1. Frequently
2. Seldom
3. Never
4. Occasionally

9. Which type of restaurant do you prefer the most?

1. Family restaurant
2. Restaurant & Bar
3. Restaurant & club
4. Any of them

10. How far do you think the restaurants in London visited by you are successful in providing best quality food and services? Rate them on the scale of 5.

1. 1
2. 2
3. 3
4. 4
5. 5

Summarised data

The data collected from the survey into the customer preferences from the target population which included the visitors at the restaurants in London consists of variety of responses received from different categories of target population which were 50 in number. In order to make decisions for opening a restaurant chain is London the responses received have been sorted and summarised.  The summarised data will be then used to draw conclusions and make analysis. The data collected and summarised is as follows:

The response to question of age group included

• 0-18 = 5
• 18-25 = 20
• 26-40 = 15
• 40-60 = 7
• Above 60 = 3

The responses included 32 male and 18 female candidates. Out of 50 responses 25 were tourists, 10 were visitors and remaining 15 were local residents of London. The employment status of therespondents was as follows:

• Government employee -5
• Private employee – 20
• Self-employed – 15
• Unemployed – 5
• Not working – 5

The income bracket of respondents included the following:

• Up to £ 2000 = 5
• £ 2001 - £ 5000 = 22
• £ 5001 - £ 10000 - 15
• £ More than £ 10000 = 3
• No Income = 5

The data related to preference in a restaurant is as follows:

• Food – 20
• Service -7
• Ambiance – 3
• Cost – 10
• Brand – 8
• Others -2

Out of the total responses received, 35 chose non-vegetarian food, 10 selected vegetarian foods and remaining 5 selected any of the two. Visit to restaurants was found frequently by 25, seldom by 17 occasionally by 8 and never by 0. Among the responses 20 preferred family restaurant, 20 preferred restaurant & bar, 5 preferred restaurant & club and rest 5 had no preference from these.  The rating of restaurants operating in London was found as follows:

• 1 = 5
• 2 = 12
• 3 = 20
• 4 = 7
• 5 = 6

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Analysis of results

The results drawn from the responses collected through questionnaire have been summarised as mentioned above. In order to draw conclusions for making decisions about starting a restaurant chain in London an analysis of the data will have to be made. For this purpose mean and median values of the data are calculated as follows: The data received about rating of restaurants includes responses as 5, 12, 20 , 7 , 6 whereas the responses of customer preference for a restaurant are 20, 7, 3, 10, 8, 2 for food, service, ambiance, cost, brand and others respectively.

Mean – It is the average value of the data and for calculating mean simple average of data is calculated by diving the sum of responses by number of elements. It is used to calculate the quantitative results of the data and is used as the average base for making decision. The mean of the given responses is calculated as follows:

Ratings of restaurants
Mean = (5+12+20+7+6)/5
            = 10

Consumer preference about restaurants

Mean = (20+7+3+10+8+2)/6
            = 8.33

Median – It represents the middle value of the collected data. It is used to calculate the qualitative results of the data for making decisions. It overcomes the limitations of mean. The median of the given responses is calculated as follows:

Ratings of London restaurants

Median = Middle value (Since n is an odd number)
= 20

Customer preferences about restaurants

Median = Average of middle value (Since n is an even number)
            = 3+10/2
            = 6.5

Mode – It represents the highest frequency of the given data. The mode of the data relating to given responses is as follows:

Rating of London restaurants

Mode = 20

Customer preferences about restaurants

Mode = 20

Analysis of data using measures of dispersion

Measures of dispersion refers to the techniques which are used to make decisions by analysing the risks and variations associated with the average results calculated for the given data or responses. These techniques include standard deviation, coefficient of variation, correlation coefficient etc.

Standard Deviation – This technique is used to calculate the risk of deviation of the average results calculated being variant. This tool will be used for analysing the risk and variation of the results from the actual in order to make decision about the opening of restaurant in London. The standard deviation is calculated as follows: Ratings of restaurants in London

Standard deviation = √ ∑dx² / n
√ 154/ 5
= 5.55

The standard deviation of 5.55shows that the mean calculated can deviate up to 5.55 on either side. Thus this risk of mean being different from the actual whether positive or negative. Thus the mean can be either 4.5 or15.5 and also it may be any value within this range. Customer preferences about restaurants

Standard deviation = √ ∑dx² / n
√ 209.34/ 6
= 5.91

The standard deviation of 5.91 of the data shows that the mean of the data may vary by 5.91 on the either side. Thus the mean can be 2.42 or 14.24 or may be any value within this range. The risk of deviation of mean of the data is 5.91. Use of quartiles, percentiles and coefficient in decision making

• Quartiles – This is the technique of calculating measures of dispersion which is used for making decision about the operational efficiency of business data collected. For this purpose the data is divided into four equal parts where each represents 1/4th value or 25% of the value part. The first quartile represents 25% of the value, second represents 50%, third represents 75% and fourth quartile represents aggregate value of 100%. It is determined that the data falls in which quartile to evaluate its efficiency.
• Percentile – This tool is used as a measure of dispersion for a set of data. For the purpose of analysis, the data is divided into 100 equal parts each representing 1/100^th part of the data. It is calculated by identifying that the data falls in which part out of 99 values to determine the efficiency. This tool is applied on large sets of data which justify the division into 100 parts. (Kolacz, 2016)
• Correlation – this technique is used to identify the relationship between two components of the given set of data. The correlation between two values can be either positive or negative and the amount of correlation is measured using coefficient of correlation. If the corresponding value changes in the same direction with the change of one value then it is positive correlation and vice versa. It is used to analyse the impact of market and other factors on the growth of business.

Task 2

Conclusions using graphs and charts

Pie Chart

Analysis - The data related to the responses of the target population was gathered from the visitors of restaurants in London. The results showed that 50% were tourists, 20% were visitors and 30 % were local residents of London. It means that most of the population in London who visit to restaurants consists of tourists and visitors. Local residents of London seldom visit the restaurants. The population that frequently visits the restaurant also consisted mostly of tourists and visitors who belonged to different countries.

Column Chart

The data collection related to age group of the population is represented by the following chart:

Analysis – The results of the data show that highest population who visit to restaurants consists of population belonging to age group between 18 years to 25 years. After this the next highest is the age group of 26-40 years. This means that most poufs the customers in the restaurant of London are young population who prefer to eat at restaurants instead of eating at home. Although the population also consists of elder people but the more focus of restaurant business shall be on the young population. (Ferres, 2013). The data related to the preference of customers while selecting a restaurant for having meals is represented by following chart:

Analysis – The data collected about the customer preference out of food, service, ambiance, cost, brand and others show that most of the population believes that food is the first priority for deciding about a restaurant. After this the second preference is given by customers to affordable cost. Then comes the services offered and the brand name of restaurant. It means that the success of business of restaurant in London will depend upon the quality of food and services over other factors.

Bar Chart

The ratings given by the population to the existing restaurants at London is represented by the following chart:

Analysis – The data shows that most of the population has given rating as 3 on the scale of 5 to the existing restaurants of London. This means that the performance of restaurants in London is average and thus there is good competition for the restaurants business in London.

Forecast using trend lines

The growth rate of restaurant sales in UK shows positive trend for opening a restaurant business which is represented by the following line chart with trend lines:

The above forecast shows that the growth rate of restaurant business in UK is highly fluctuating and is affected by various market factors. Thus the restaurant business is risky but has a high potential. The forecasted growth rate for the next two years is positive. Although the growth rate in next two years is not likely to reach the highest peak but in long run it will be sustainable. (Rivera, 2013)

Business Presentation

Formal Business Report

To,
The Board of Directors
ABC Restaurant Group
Date – 29 June, 2016

Subject: Presentation of information about potential of restaurant business in London.

It is to provide the information to the Management that a survey has been conducted into the customer preferences of the population who frequently visits to restaurants in London. The method used for collection of data was filling up of questionnaire by the population. The responses received have been analysed and valid conclusions have been drawn based on the analysis of data. On the basis of these conclusions this report is hereby prepared to provide relevant information to enable you make decision about opening the second restaurant chain in London.

The forecast of growth rates for the next two years of sales of restaurant business reveals that the growth rate is likely to be positive and the decrease is not much. Apart from this the ratings given by the customers to the existing restaurants in London is average which means that the market is competitive and success of business depends on the quality of food and services which has been given the highest priority by customers while deciding about the restaurant. Also most of the population who eats at restaurants in London comprises of young people between the age of 18 to 40 years. Thus more focus of the marketing and promotional strategy of the business shall be on this target population in the market. 

Therefore the management shall consider the information provided in this report while making decision about opening a second restaurant in London. (Special Report, 2015)

Regards
Research Manager

Project Plan and Critical Path

The project plan consists of a detailed plan for the business process of restaurant in London to serve the customers. The different activities involved and the time taken by each activity in minutes is as follows:

Critical Path = 
=55 minutes

Evaluation of financial viability

The financial viability of a project relates to the efficiency of the project about its financial performance. The tools which are used to measure the financial viability of the project are Net Present Value method, Discounted Cash Flows, Payback period etc. The estimated cash flows ad net present value of the project for opening a restaurant in London by an existing restaurant chain is as follows:

Estimated cash outflow = 700,000
Estimated cost of capital = 11 %

	Cash Inflow	Cumulative	P.V. Factor	Present Value
0	(700,000)	0	1	(700,000)
1	250,000	250000	0.901	225250
2	200,000	450000	0.812	162400
3	170,000	620000	0.731	124270
4	150000	770000	0.658	98700
5	130000	900000	0.593	77090
6	130000	1030000	0.535	69550
				57,260

Net Present value= 757,260 – 700,000 = 57,260
Payback Period= 3 + (670– 620)÷ 150= 3.33 years or 3 years and 3 months

The project is having appositive Net Present Value therefore the investment can be made in the project. The payback period of the project is 3 years and 3 months which means that the project is capable of recovering the initial cash outflow within 3 months. (Abraham, 2014)

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Conclusion

From the Unit 6 Business Decision Making for Restaurant Assignment which discusses about the customer preferences for restaurants in London, it can be concluded that the opening of restaurant in London is a profitable business. Since the organization who proposes to open the restaurant is an existing restaurant chain therefore it is a good opportunity for it to earn huge profits. The evaluation of financial viability of the project also suggests that the project is financially viable since the net present value is positive.

References

"SPECIAL REPORT: BUSINESS EDUCATION; NEW PROGRAMS", 2015, Crain's Detroit Business, vol. 31, no. 22, pp. M043.
Abraham, A. 2014, Project Planning and Management: An Aspect of Development, 1st edn, Anchor Academic Publishing, DE.
De Leeuw, E.D. 2013, "Thirty Years of Survey Methodology / Thirty Years of BMS", Bulletin de Méthodologie Sociologique, vol. 120, no. 1, pp. 47-59.
Drob, C. 2012, "Overview about project planning", Studies and Scientific Researches : Economics Edition, , no. 16.
Ferres, L., Lindgaard, G., Sumegi, L. & Tsuji, B. 2013, "Evaluating a Tool for Improving Accessibility to Charts and Graphs", ACM Transactions on Computer-Human Interaction (TOCHI), vol. 20, no. 5, pp. 1-32.
Gionis, A. 2013, "Data Analysis", Data Science Journal, .
Kazi, A.M. & Khalid, W. 2012, "Questionnaire designing and validation", JPMA. The Journal of the Pakistan Medical Association, vol. 62, no. 5, pp. 514.
Ko?acz, A. & Grzegorzewski, P. 2016, "Measures of dispersion for multidimensional data", European Journal of Operational Research, vol. 251, no. 3, pp. 930-937.
Manikandan, S. 2011, "Measures of dispersion", Journal of pharmacology & pharmacotherapeutics, vol. 2, no. 4, pp. 315-316.
Miles, M.B., Huberman, A.M. & Saldaña, J. 2014,Qualitative data analysis: a methods sourcebook, Third edn, SAGE Publications, Inc, Thousand Oaks, Califorinia
Rivera Peruyero, J.R. & Marti-Puig, P. 2013, "Automatic trend line generation for time-series analysis: A graphical approach", , pp. 189.
Schoenduwe, R., Mueller, M.G., PetersA. & Lanzendorf, M. 2015, "Analysing mobility biographies with the life course calendar: a retrospective survey methodology for longitudinal data collection", Journal of Transport Geography, vol. 42, pp. 98-109.

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