Analysis of Profit Rate Distribution in Manufacturing Companies
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This report analyzes the distribution of profit rates in manufacturing companies and compares them based on size and export activity. It also examines the relationship between profit rate and other quantitative variables.
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Table of Contents INTRODUCTION...........................................................................................................................3 MAIN BODY...................................................................................................................................3 a) Distribution of the profit rate around the organisations in the data set....................................4 b) Assessment of the distribution rate with comparison to large and Small and medium size enterprise......................................................................................................................................5 c) An analysis of the distribution of the profit rate, comparing manufacturers in the data set by their export activity......................................................................................................................6 (d) An analysis of the distribution of Return on Capital Employed comparing large and SME manufacturers in the data set........................................................................................................7 (e) An analysis of the export activity ofthe manufacturers in the data set against their size, measured by whether they are large or SME companies.............................................................9 (f) An analysis of the extent to which the profit rate across all manufacturers in the data set is associated with the other quantitative variables in the data set.................................................11 (g) An analysis of the extent to which the profit margin for manufacturers in the data set that can be predicted using the other quantitative variables in the data set......................................11 CONCLUSION..............................................................................................................................13 REFERENCES..............................................................................................................................14
INTRODUCTION The term quantitative skills for business can be defined as critical analysis of behaviour and skills of various types of quantitative data (Shieh and Jan, 2015). This shows a monetary aspect along with various types of financial techniques and projected measures. The report covers detailed information about analysis of statistics of business for investors who are going to make invest in various types of industries such as wood, furniture etc. This analysis is being done by help of evaluation of given data series of companies. Basically, the distribution of data is being compared with production department under series of data by export activities. In addition, the project report shows an evaluation of return on capital employed (R. O. C. E.) to small medium enterprise and big manufacturers which is subject to data set. The profit rates from different producers with provided data series also assessed with variable data series. Basically, the main goal of this project report is to give a complete analysis of monetary performance of wood, furniture and paper manufacturing sectors (Reid and Wilkes, 2016). Under the report, different types of statistical methods are applied for computing favourable outcomes such as descriptive analysis technique, histogram, correlation, Chi squared test by complete explanation. These all techniques are used on the given data set to do a proper analysis. MAIN BODY Industry overview: Due to uncertainties in the economics various types of challenges are raised for various segments such as furniture, paper and wood sectors in the entire United Kingdom. Because of Brexit, exporting cost has been raised and became a main issue for investors who are going to invest in the furniture and manufacturing businesses (Wang, Xiong and Shi, 2016). As well as the manufacturing sector of paper and paperboard largely relays on the certain degree on labour uprise from European Union. In accordance of National statistics office, in year 2017, products of petroleum has been raised by 7% of United Kingdom industrial segment. On the other hand, European labour considered into account for about 11 % of both United Kingdom mining sector's work force (About industry review of United Kingdom, 2019).
a) Distribution of the profit rate around the organisations in the data set. The analysis of distribution of profit is too crucial in order to assess efficiency of profitability. The below analysis shows the understanding of rate of profitability along with set of data of other companies. Herein, below a table is presented which shows the descriptive statistics of margin of profitability for 565 business entities regards to wood, paper and furniture manufacturing company. The below mentioned table shows average rate of profit is of 5.02 % along with standard deviation which is of 6.91 %. The variance is of 47.66 and skewness test of 1.66 and Kurtosis is of 6.48. Descriptive Statistic NumbersRangeLowerHigherMeanStandard Deviatio n VariancesSkewnessKurtosis StatisticsStatisticsStatisticsStatisticsStatisticsStatisticsStatisticsStatis tics Stan dard Error StatisticsStandard Error Margin of profitabil ity(In termsof %) 56560.90%-13.08%47.82%5.02%6.91%47.661.660.16.480.2 ValidN (Serial wise) 565 Analysis- On the basis of above prepared table, this can be find out that lower profit margin is of -13.08 % and higher is of 47.82%. On the other hand, the mean of profit rate is of 5.02% and standard deviation is of 6.91%. The value of variances is of 47.66. This detailed analysis is being on data series of 565 numbers of companies. The above mentioned analysis shows that produced result is quite satisfactory for shareholders because rate of profit is sufficient. If shareholders will invest in this sector then it can be beneficial for them.
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b) Assessment of the distribution rate with comparison to large and Small and medium size enterprise In the given data it is assessed by help of difference with large and medium sized business entities. Analysis- On the basis of above presented graph, it can be find out that higher variability of margin of profit is of 5.02%. This variability is with the average profit rate and standard deviation of 6.90 of 565 business enterprises. The given sheet of data of 565 companies indicates that making investment in medium and small sized companies will be beneficial. It is so because their produced results are feasible and suitable. Though, efficiency of large scale business entities is less effective in compare to small and medium sized enterprises. Hence, the investors should draw their attention towards making investment in small and medium sized business entities.
c) An analysis of the distribution of the profit rate, comparing manufacturers in the data set by their export activity. Analysis- On the basis of above presented graph, this can be find out that analysis is done on data set of 565 business entities. This shows that value of mean is of 5.0294 % as well as standard deviation is of 6.90401. It presents that investing in the small and medium size enterprises can be effective and beneficial for client. This is so because rate of profitability is much more in compare to large sized business entities.
(d) An analysis of the distribution of Return on Capital Employed comparing large and SME manufacturers in the data set. Descriptive Statistics MeanStandard DeviationNumber Smallmedium enterpriseindicator 1=SME Size 2=Large 1.220.41565 ReturnonCapital Employed (%)14.9222.77565 Correlation Smallmedium enterprise indicator 1=Small medium enterpriseSize 2=Large Returnon Capital Employed (%) Smallmedium enterpriseindicator 1=SME Size 2=Large Pearson Correlations1-.025 Sig. (2-tailed).546 Sum of Square and Cross-product96.22-134.986 Co-variance.171-.239 N565565 ReturnonCapital Employed (%) Pearson Correlations-.0251 Sig. (2-tailed).546 Sum of Square and Cross-product -134.986292348.255
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Analysis – On the basis of above presented graph, this can be find out that there is difference in performance of large and SME organisations in terms of return on capital employed. Such as the mean of return on capital employed is of 14.92 as well as standard deviation of return on capital employed is of 22.767. It shows that large sized companies will provide more return as compare to small and medium sized business entities. (e) An analysis of the export activity ofthe manufacturers in the data set against their size, measured by whether they are large or SME companies. Chi-Square test- It can be defined as a type of statistical tool which is used in order to measure how expectations in compare to actual assessed value of data (Hesamian, 2016). This data is being applied in computing a chi-square statistic should be random, raw and drawn from independent variables and from a long enough sample. Chi-Square Tests ValueDegree of freedom (D.F.) Asymptotic. Sig.(Two- sided) ExactSig. (Two-sided) ExactSig. (One-sided) Pearson Chi-Square8.983a1.003 Continuity Correctionb8.3821.004 Likelihood Ratio9.0321.003 Fisher's Exact Test.003.002 Linear-by-Linear Association8.9671.003 N of Valid Cases565 a. 0 cell (0.0%) have expected count less than 5. The minimum estimated count is 60.30. b. Calculated only for a 2x2 tables
Exporter 1 = Domestic sales only0 = Export Sales x Small medium sized enterprises indicator1= Small medium sized enterprise2= Large Cross tabulation Count Smallmediumsize enterprisesindicator 1=Small medium enterprise, Size 2=Large Total 12 Exporter1 = Domestic sales only0 =Export Sales 020275277 124048288 Total442123565 The above table shows information regards to data of domestic level sales and big production entities. The data shows that domestic sales distributors are assessed of 202 and domestic sales of export is analyzed as 240.
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Analyse – On the basis of above mentioned graph, this can be find out that small and medium sized business entities are performing well as compare to large sized businesses. It is so because above bar graph showing this as ratio of total value of exporting is of 75 while large organisations' of 40. This signs that small and medium enterprises' performance is much more better. As well as it can be find out that efficiency of exporters in term of wood, paper and furniture production companies is different. Basically, the importance of evaluation shows towards analysis of various types of variables as the number of producers with export practices. As well as above test shows Pearson chi square along with volume of 8.983 the assigned importance variation of 0.03 with extracted value of 0.004. As well as analysed ratio shows the key difference of 0.003. In addition, Linear equation was evaluated as 8.967 with key difference of 0.002. (f) An analysis of the extent to which the profit rate across all manufacturers in the data set is associated with the other quantitative variables in the data set. Coefficientsa ModelUN-standardized Coefficients Standardized Coefficients tSig. BStandard Error Value of Beta 1 (Constant)2.1770.752.8960 Number of employee000.110.810.42 Return on Shareholder Funds ( in %)-0.010.01-0.08-1.7580.08 ReturnonCapital Employed ( in %)0.180.010.5812.9550 Cost of Production (in £)-4.752E-006.000-0.14-10.32 Salary as accordance of percentageoftotal turnover percentage 0.010.030.010.230.82 Credit limit (£)4.06E-00700.214.2940 a. Dependent Variable: Margin of profitability (in terms of %) Analysis- The above presented table shows the rate of profitability across all manufacturers. The table is categorised into two parts which are unstandardised coefficient and standardized
coefficient. In the aspect of return on shareholders' funds is different in both of categories. Such as under unstandardised coefficient this is of -0.01% and 0.01%.As well as the value of standardized coefficient is of -0.08%. The percentage of return on capital employed is of 0.18% and 0.01 % in standard errors. While in the standardized coefficient, this is of 0.58 %. Hence, the above analysis shows that efficiency of small and medium sized companies is much more better in compare to large sized businesses. (g) An analysis of the extent to which the profit margin for manufacturers in the data set that can be predicted using the other quantitative variables in the data set. ANOVA TEST Additionof Square Degrees of freedom Mean SquareFSig. Number of employee Between Group1661856208.6 404773483975.2870.6240 Within Group4291820.1678749331.266 Total1666148028.8 07564 ReturnonShareholder Funds ( in terms of%) Between Group1568619.4364773288.5101.5610.01 Within Group183253.336872106.360 Total1751872.772564 Costofmanufacture (value in £) Between Group23160712061 328.44047748554951910. 5426.1460 Within Group68731336513 9.250877900153622.2 90 Total23848025426 467.690564 R. O. C. E. (in terms of %) Between Group267640.422477561.0911.9760 Within Group24707.83387283.998 Total292348.255564 Between Group35840.94547775.1380.920.71
Salary as accordance of percentageoftotal turnover percentage Within Group7113.0838781.760 Total42954.028564 Credit limits (in £) Between Group67812733236 55168.00047714216505919 612.51214.5570 Within Group84963953150 838.6708797659716265 3.318 Total68662372768 06007.000564 Analysis- On the basis of above done ANOVA test, this can be find out that in the number of employees, value of mean square is of3483975.280 between group and within group this is of 49331.27. As well as the total value of degrees of freedom is of 564. In addition, the value of return on shareholders fund is of 1568619.436 between the group and within group, this is of 183253.336. Its total is of 1751872.772. On the other hand, degree of freedom is of 477 and 87 of between & within groups. Mean square is of 3288.510 and 2106.360. Cost of manufacture- The sum square of cost of manufacturing is of 23160712061328.440 and 687313365139 for between and within of groups. Along with degree of freedom is of 477 and 87 which is similar as above value of return on shareholder funds. As well as the value of mean square is of 48554951910.542 & 7900153622.290. Return on capital employed (ROCE)-The sum square of return on capital employed is of 267640.422 and 24707.833 for between and within of groups. Along with degree of freedom is of 477 and 87 which is similar as above value of return on shareholder funds. As well as the value of mean square is of 561.091 and 283.998. Salary as accordance of percentage of total turnover percentage-The sum square of return on capital employed is of 35840.945 & 7113.08 for between and within of groups. Along with degree of freedom is of 477 and 87 which is similar as above value of return on shareholder funds. As well as the value of mean square is of 75.138 % and 81.760 %. Creditlimit-Thesumsquareofcreditlimitisof6781273323655168.000and 84963953150838.670 for between and within of groups. Along with degree of freedom is of 477
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and 87 which is similar as above value of return on shareholder funds. As well as the value of mean square is of 14216505919612.512 & 976597162653.318. CONCLUSION On the basis of above presented report, it has been concluded that all forms of industries including furniture, paper etc. are performing well. In order to make a proper analysis a vital range of techniques are applied. Such as descriptive statistics, ANOVA testing and many more. In the aspect of efficiency of providing return on capital employed, large sized companies are much more better in compare to small and medium sized companies. As well as in terms of gaining return on profitability, the large sized companies are effective. So investors should make investment in large sized enitites.
REFERENCES Books and journal: Shieh, G. and Jan, S.L., 2015. Optimal sample size allocation for Welch’s test in one-way heteroscedastic ANOVA.Behavior research methods.47(2). pp.374-383.. Hesamian, G., 2016. One-way ANOVA based on interval information.International Journal of Systems Science. 47(11). pp.2682-2690. Wang, R., Xiong, Z., Liu, J., Xu, J. and Shi, L., 2016. Chi-square and SPRT combined fault detection for multisensor navigation.IEEE Transactions on Aerospace and Electronic Systems. 52(3). pp.1352-1365. Reid, J. and Wilkes, J., 2016. Developing and applying quantitative skills maps for STEM curricula,witha focuson differentmodesof learning.InternationalJournal of Mathematical Education in Science and Technology. 47(6). pp.837-852. Online: AboutindustryreviewofUnitedKingdom,2019.[Online].Available through:<https://www.unece.org/fileadmin/DAM/timber/country-info/statements/ unitedkingdom2017.pdf>