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The business decision making assignment report aims at collection of primary and secondary data of Marks and Spenser a retail store by conducting surveys and applying various methodology of investigation. The reports also includes various measures of central tendency and further making effective correlation between total revenue of group business of M&S and revenue of general retail business strategy. By using effective charts, graphs and trend charts which is an effective tool of future forecasting along with time series analysis for total group revenue and general merchandise revenue for the year ending 2016, 2017 and 2018.
After planning and making investigation on the market strategy on Mark & Spenser the investigator looks on gathering the desired information or the collection of data
Primary data are those data, which is collected by the investigator from the external market situations. These primary data collected through various sources are original in nature and it gathered. These data are called primary because they are collected from the very source form where information is captured. Same data can be primary and secondary for two different organisations. In our case of Marks and Spenser primary data is collected through routine small interviews with the customers visiting store and by questionnaire. It is seen many times that companies with FMCG sectors offer reward coupons for filling questionnaire. Other methods used by the company to collect primary source of database are conducting surveys about the store among the prospective customers, making observations on the basis of external market situations and prepare reports accordingly and finally making reports on the past case studies with different prospects (Najera, et. al., 2012).
Secondary data are those data which is gathered from database agencies. Such type of data is already collected by some other people whose primary job is to find database. This database is separated from its origin source and changes are made in that database to make it useful for the organisation grabbing it. That data collected from the people who had primarily investigated will become secondary for the other people who may be utilising it. For instance the population census data is collected by government department is the primary data for the government and the organisation utilising that data will sense it as secondary data as it is not primarily collected by those organisation. The statistical data which is new in the hands of investigator are primary in nature while the same data utilised by the organisation will become secondary. In our case of Marks and Spenser data collected from various database collection agencies which will help the company in determining its strategic policies and future goals and objectives of the organisation. Further it helps in grabbing the prospective consumers by having complete data of them, their taste and preferences, choices etc. Strategic policies are decided by viewing the financial statements of the competitors which is received from their annual reports. Annual reports of the other companies will be secondary data for the company (Najera, et. al., 2012).
Under this method information is taken out from each and every element of the universe related to the business environment. General information is gathered which is applicable to all firms of the respective industry. For example if we want to know the general salary and work satisfaction level of the employees working in FMGC sector or in retail business environment then a general survey is to be conducted. There is common information gathering analysis is conducted which helps in making a response to the resulted survey. These are questioners, personal interviews with the employees working in the FMGC sector (Holland, et. al., 2013).
A sampling in case of Marks and Spenser is gathering information from the selected units representing the mass out of whole population or universe. Every sample should have the inheriting characteristic of the universe. If we want to know the customer returns of goods in the Marks and Spenser stores then total sales shall be considered as universe and each return as a unit or an element. We will find the percentage of returns that generally occur on total sales of a year and the outcome will be applied for each upcoming year. Multistage sampling and stratified sampling methods of the random sampling will we used in obtaining the data from various sources (Holland, et. al., 2013).
Name ______________________________________________
Mobile number____________________________________________
Address___________________________________________________
Gender____________________________________________________
1. Do retail store has sufficient parking space
Excellent
Good
Average
Below average
Suggestion ___________________________________________________
2. Whether prices of products offered at store are at relevant prices
3. Whether behaviour of the staff at the store was nice to you
4. How was the physical appearance of the store?
5. How was the general quality of products offered by store?
6. How is free defect return policy of the Spencer store?
7. Rate on working hours of store
8. Rate on cashless facility of store like acceptance of credit card, Debit card and availability of ATM’s and point of sales machine
9. Rate on employee attention to attend every individual customers
10. Rate the shopping bag, catalogue provided by store in terms of appearance
11. Rate on the level of safety that customers feel in buying goods form Spenser’s store.
12. Rate on the complain management and solutions provided by store management to its customers.
13. How often you visit the store
14. What products and general items you buy from the store
15. Would you recommend our products to others?
Is there anything else you want to specify about the quality of the store and behaviour of the employees? Also provide your valuable suggestions.
Arithmetic mean The arithmetic mean is the most widely used and most generally acceptable of all the available averages. It is calculating by adding the total values of all the respective years or series of information divided by no of items to find an average of the number (Backes & Rose, 2010). There are two types of Arithmetic mean
Mean= total value of items/ no of items
(Adkins & Paxson, 2014)
Median Median is position average of ascending or descending arrangements of database. It is mid value of the above arranged data. Its value is located in the frequency distribution table at the mid level with 50 % of the items at the downstage part and 50 % of items at upward part.
Formulae of calculating median is
M= value of {(n+1)/2} term
M= median
N= number of item
Median of value of total revenue
= (10+1)/2
=6^{th} term
The value at 6^{th} term is revenue of the year 2010 that is 9536.6 (Adkins & Paxson, 2014)
Median of value of merchandise revenue
= (10+1)/2
=6^{th} termThe value at 6^{th} term is revenue of the year 2008 that is 4059.3
Arranged the data in ascending order for the total revenue and merchandise revenue for calculating median and mode
(Adkins & Paxson, 2014)
Mode – mode is the value of a particular observation of total revenue and merchandise revenue of marks and Spenser which occurs with greatest frequency and thus is the most desirable value. The mode item is the most representative or dominant in the complete frequency table. The data is first of all arranged in ascending or descending order. Mode can be located in individual observations by the following ways
In case of total revenue and merchandise revenue no two figures are repeating therefore it is not possible to calculate mode in a statistical distribution (Adkins & Paxson, 2014).
Measures of dispersion
Standard deviation
The standard deviation is the most important and most important method of dispersion. It is widely different from mean deviation which can be computed from any measure of central tendency be it be mean, median or mode here in standard deviation it is always computed from the arithmetic mean. While taking deviation from arithmetic mean plus minus signs are ignored and then these deviation are squared up and totalled the sum of square of deviations are divided by number of items (Saxena, 2015).
The standard deviation of total revenue and merchandise revenue are as follows
Quartiles are the tools which divides the total data into four equal parts. First quartile which is also known as lower quartile and it is represented by 25 % of data. Second quartile or median of the data which is provisional average of the data, it is measure of partial range which is calculated by deducting the value of lower quartile. This measure takes only middle 50% of the items. The frequency of class interval of the database is also given importance of the computation of second quartile (Saxena, 2015). The quartiles of the total revenue and merchandise revenue from the year 2005 to 2015 is tabulated below
From the above analysis of marks and Spenser total revenue and merchandise turnover for the past 10 years report and on making an analysis we came to know that quartile 1 of the total revenue makes part of 8.64 & 8.98 % percentage of the total revenue and merchandise turnover.
Similarly quartile 3 shows 9.3 & 9.2 % of total revenue and merchandise turnover (de Motta & Ortega, 2013).
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Call us: +44 – 7497 786 317One of the methods of calculating coefficient of correlation was given by Karl way back in the 19^{th} century. It was based on covariance of concerned variables. There are two broad categories of formulation which are used by the Karl for calculating coefficient of correlation. These are as under
Direct Method It is measured by expressing the ratio of covariance to the standard deviation of the two variables. It is an effective measure to create relative relationship numerically
Shortcut methods in these method deviations are taken from the actual arithmetic assumed mean and apply the further steps as given in direct method.
Correlation Coefficient 
0.825808507 
This helps in creating relationship between two distinct variables, total revenue and merchandise revenue of Marks and Spenser in our case. It helped in finding out the relationship between the variables and coefficient of the above mentioned data. The correlation 1 represent the perfect correlation as in our case correlation between the total revenue and merchandise revenue is 0.826 which shows deviation from the perfect correlation it means total sales of total revenue and merchandise revenue are not perfectly correlated. Change in the data of turnover of one unit will have not a perfect impact over the turnover of merchandise and vice versa (de Motta & Ortega, 2013).
Time series graphs – one of the most important factors to keep in mind while studying the statistical data of an organisation is analysis of data over the period of time and determining its trend. The data are related to time observation are taken at specific intervals. These intervals may be year, month, week, day, hour, minutes or sometimes even seconds. In our case of Mark and Spenser representation of annual turnover is made through time series graphs. In this graphical representation through a pictorial approach for better and easy representation of data
From the above chart it is analysed that there is continuous increase in total revenue from 2005 to 2015. Revenue has been continuously increasing over the period of time. It can be further analysed that company is continuously growing with high pace
While analysing the charts of merchandise revenue there has been decrease in merchandise revenue since 2012. Further there have been huge fluctuations in the turnover of the merchandise over the period of time. We can observe continuous ups and down in the particular activity (Jarrow, 2014).
Scattered graphs
It helps to create the relationship between the total revenue and merchandise revenue. We observed that total revenue is in upward direction but the merchandise revenue is in fluctuating way. It reflects how changes in one variable affect the other (Jarrow, 2014).
It is future projections of data using the past analysis. In our case of total revenue and merchandise revenue of Marks and Spenser trends for the year 2016,2017 & 2018 will be calculated as follows

For total revenue 
For merchandise revenue 
2016 
Year 2016 y= ax+b =281.5*13+7567 =$10945

Year 2016 y= ax+b =39.78*13+3763 =$4240.36

2017 
Year 2017 y= ax+b =281.5*14+7567 =$11226.5

Year 2017 y= ax+b =39.78*14+3763 =$4280.14

2018 
Year 2018 y= ax+b =281.5*15+7567 =$11508

Year 2018 y= ax+b =39.78*15+3763 =$4319.92

A problem of critical path analysis is given with standard working days in a week given in the problem is 5 days in a week as bottleneck
Findings
The following is the duration of each activity along with beginning and ending activity of each project
No. 
Beginning task 
Descriptions 
Duration(working days 
Constraints 
1 
A 
Requirement analysis 
5 
 
2 
B 
System design 
15 
A 
3 
C 
Programming 
25 
B 
4 
D 
Telecoms 
15 
B 
5 
E 
Hardware installation 
30 
B 
6 
F 
Integration 
10 
C,D 
7 
G 
System testing 
10 
E,F 
8 
H 
Training/Support 
5 
G 
9 
I 
Hardware and GoLive 
5 
H 





(Olawale, et. al., 2010)
Calculation of critical path by analysing various paths on the network
The critical path on any network is the highest duration taken among all the available paths in which project will be completed. It is the shortest time in which project will be completed. In our case the above network diagram gives us three paths among which path 1 that is ABCFGHI takes the highest time of 75 days in reaching the end activity (Olawale, et. al., 2010). Therefore the critical path of our project is 75 days.
As it is given in our problem that worker are working 5 days in a week which is bottleneck or constraints to our problem. Further it is analysed that critical path in our situation is 75 days. The planned duration of the project in weeks is = 75/5= 15 weeks. It will require minimum of 15 weeks to complete our project. So we should plan our activities accordingly (Akkoyun, 2012).
Non critical tasks are those tasks in which any reduction in the possible days of the project will not affect the life span of the project. In our case path 2 & path 3 are non critical paths. These are ABDFGHI & ABEGHI. At the time of crashing it is carefully analysed that the critical path will not become non critical. Other prospects which is also seen that total float will not be zero on noncritical activities.
References
Adkins, R. & Paxson, D. 2014, "Stochastic Equipment Capital Budgeting with Technological Progress", European Financial Management, vol. 20, no. 5, pp. 10311049.
Akkoyun, O. 2012, "Simulationbased investment appraisal and risk analysis of natural building stone deposits", Construction and Building Materials, vol. 31, no. 1, pp. 326333.
Backes, G. & Rose, V.J. 2010, "Primary and secondary analysis of local elected officials' decisions to support or oppose pharmacy sale of syringes in California", Journal of urban health and care : bulletin of the New York Academy of Medicine, vol. 87, no. 4, p. 553560.
de Motta, A. & Ortega, J. 2013, "Incentives, Capital Budgeting, and Organizational Structure", Journal of Economics & Management Strategy, vol. 22, no. 4, pp. 810831.lmassri, M.M., Harris, E.P. & Carter, D.B. 2016;2015;, "Accounting for strategic investment decisionmaking under extreme uncertainty", The British Accounting Review, vol. 48, no. 2, pp. 151168.
Holland, C.L., Bowker, L.K. & Myint, P.K. 2013, "Barriers to involving older people in their resuscitation decisions: the primary–secondary care mismatch highlights the potential role of general practitioners", International Journal of Clinical Practice, vol. 67, no. 4, pp. 379384.
Jarrow, R. 2014, "Computing Present Values: Capital Budgeting Done Correctly", Finance Research Letters, vol. 11, no. 3, pp. 183.
Najera, D.A., McCullough, E.L. & Jander, R. 2012, "Interpatch foraging in honeybees—rational decision making at secondary hubs based upon time and motivation", Animal Cognition, vol. 15, no. 6, pp. 11951203.
Olawale, F., Olumuyiwa, O. & George, H. 2010, "An investigation into the impact of investment appraisal techniques on the profitability of small manufacturing firms in the Nelson Mandela Metropolitan Bay Area, South Africa", African Journal of Business Management, vol. 4, no. 7, pp. 1274.
Saxena, A.K. 2015, "Capital budgeting principles: bridging theory and practice", Academy of Accounting and Financial Studies Journal, vol. 19, no. 3, pp. 283.