Comparison Of Clothing Manufacturing Business In Britain And Germany For Productivity, Machinery And Skills

Comparison of Business Models in Britain and Germany

Discuss about the Design Of Experiments For Engineers And Scientists.

The study of business model provides the way for improvements in the future policies. Also, comparison of the same business model in two different countries gives us an idea of doing business at different places in the globe. Here, we have to compare the clothing manufacturing business in the Britain and Germany for the productivity, machinery and skills used for the manufacturing process. This study compares the business models for the manufacturing of cloths in the two countries by using the samples of matched plants. This study is focusing on the manufacturing of the women’s outerwear in these two countries. It was observed that the German clothing manufacturers produces the high fashion items in great variety, however the British manufacturer produces more standardised items which compete the lower cost producers in developing countries. This study consists of the comparison of the contribution of machinery, new technology, and skills to differences in clothing production in the two countries Britain and Germany. Random samples are taken from the manufacturing units from the both countries and then these samples or collected data are analysed by using statistical methods for comparing the contribution of machinery, technology and skills in both countries. It was observed that German producers manufactured the higher value added cloths. Also, it was observed that most of British producers concentrate on the production of long runs of simpler standardized styles to low wage countries. It was also observed that the about 80% of the manpower in the Germany have two to three year course in cloth manufacturing, but manpower in Britain cloth manufacturing do not have much education in cloth manufacturing.

A research study was performed for the comparison of the productivity, machinery and skills for the clothing manufacturing in the two countries such as Britain and Germany. It was revealed that the German manufacturing develops high fashion designs with high quality, while the Britain cloth manufacturing develops the old fashions, simple and long run usable cloths which they exports in developing countries. The comparison of the two manufacturing industries in cloths was performed by using the comparison of randomly selected samples. It is observed that the German cloth manufacturing industry uses the high technology machines and skilled manpower for the production of high value added fashion designs of the cloths, however the British cloth manufacturing industry use low technology as compared to German cloth manufacturing. Also, British cloth manufacturing does not use as much skilled personnel in their industry for the manufacturing of cloths. It is observed that use of high technology manufacturing of cloths do not get more profits as compared to regular manufacturer cloth industries. In the sense of business profits, the cloth manufacturing industry in Britain gain more revenue as compared to German cloth manufacturing industry.

Productivity, Machinery and Skills in Clothing Manufacturing

For the given business model of cloth manufacturing, a comparison was carried out for industries in Britain and Germany. For this comparison purpose, random sample data was used. A comparison was carried out by using different manufacturing plants in both countries. Different types of plants involved in the study. Plants were distributed according to their size of employees such as 0 to 20 employees, 20 to 100, 100 to 200, and more than 200 employees. Also, percentages of employees were calculated for all clothing plants and women’s outerwear plants for both countries. Median number of employees in Britain cloth manufacturing plants is found as 127, while median number of employees in German cloth manufacturing plants is found as 90. For the women’s outerwear plants, median numbers are found as 100 and 85 for Britain and Germany respectively. There is no significant difference found in the plant size for women’s outwear plants in both courtiers.

With regarding the machinery used in the cloth manufacturing, it was found that British made machineries are negligible in both countries. German manufacturers used predominantly German machineries which are renewed. These machines have high technology with auto control functions. British manufacturers do not use advanced machineries as compared to German industries. Minority of British plants used the German made machineries for their production.

Now, we have to compare the qualification of the workforce used in the German and British cloth manufacturing plants. The study based on the sample data revealed that over 80% of the German machinists in the plants had completed two or three year degree in cloth manufacturing skills. There are ten times more such graduates in Germany as compared to Britain. In the sample study, no single machinists in the Britain were found with such eligible degree in cloth manufacturing skills. Due to well educated and skilled workforce used in Germany, highly fashionable cloth products were manufactured.

Now, we have to compare the quality of the cloths in manufacturing industries in Germany and Britain. Actually, we cannot compare quality of the products on the same base, because German industry manufactured highly fashionable high value added cloths; while Britain industry mainly produces simple and long run usable cloth products. British industry aims to compare their products with cloths produced in the developing countries, because most of products they exports to developing countries.

From this research study based on the cloth manufacturing industry in the Germany and Britain, the findings for this research study are summarised as below:

1. Machineries with high technology are used in German cloth manufacturing industries as compared to British cloth manufacturing industries.
2. More skilled manpower was used in the German cloth manufacturing industries as compared to British cloth manufacturing industries.
3. German manufacturers produce high value added fashionable quality cloth products, while British manufacturer produces simple, long run usable quality cloth products.
4. Comparable revenue is more for the British cloth manufacturer as compared to German cloth manufacturer, because German manufacturer expense more on technology and high qualified staff while British industry use optimized model for their production.

Optimization Techniques for Increasing Business Profit

For the purpose of increasing the profit or revenue in the cloth manufacturing industry, we use some optimization techniques. It is important to involve all significant variables in the model so that we get significant and unbiased results for this research study. In the optimizing technique, we will consider all constraints that affect the profit and revenue of the cloth manufacturing industry. The variables such as cost of raw material, type of machineries used, qualification of working staff, operating schedules, rate of defective items, etc. We will consider linear programming for the evaluation of optimum model. In the first step, we will decide and study all parameters involved in the business process. It is important to consider all eligible significant parameters related to the business of cloth manufacturing. It is important to produce the items, which will attract their customers. For every business industry, it is mandatory to create their customer base for expanding their business. So, it is important to consider current trends in the customer choice. So, customer survey is also important for the creation of good business model. We will consider all significant variables for the model. We will use linear programming for optimization of the business. Linear programming will allow us to increase or decrease the level of parameters for getting specified benefits. This model helps us in maximizing the profit within available resources. For the application of this model, we need to collect the data. For the collection of data, we will use randomization. Total enumeration is not possible due to time and expensive cost of the research study. We will consider random sample data for all variables involved in the model. Firstly we will collect data by using proper methods. If we will not use proper measurements for the variables used in the data, then there would be biased results. Also, it is important to handle data carefully for getting unbiased results. Missing observations should be eliminated from the sample data. Sample sizes should be adequate to make inferences about the cloth manufacturing business. After the collection of random sample data, we will analyse this data by using proper statistical methods. We will use excel for the analysis of linear programming model for given data. After using this model, we will interpret the results in systematic way and then we will make better decisions for the cloth manufacturing industry.

We know that every industry is related to more than one or two variables. The product of the manufacturing industry is the effect of more than one variable. So, we will consider multivariate linear regression model for the prediction of profit of the cloth manufacturing industry. By using this model, we will predict the profit of the business based on all dependent variables. The choice of proper dependent variables is important for getting statistically significant results. If the variable is not statistically significant, then we will eliminate this variable and we will again this regression model. Before using this multiple regression model, we will check all assumptions required for this regression model. This model will provide estimates for the response or dependent variable which will be useful for making decisions about independent or explanatory variables. For this regression model, we will use all related variables and we will check the significance of every independent variable separately. We will find out the pattern of relationships exists between the dependent variable and independent variables. Before using this multiple regression model, we will collect the data from relative industries. We will use proper randomization techniques for the collection of data. We will avoid things that will responsible for getting biased results. It is important to collect data by using proper method. Sometimes, we need to collect data by using survey methods. Instead of using survey data, we will try to use official data from the cloth manufacturing units or plants. After collection of data, we will run multiple regression model by using any statistical software and then we will interpret the results of the regression model.

Multivariate Linear Regression Model for Predicting Business Profit

We will check whether how much variation in the dependent variable is explained by the independent variables involved in the regression model. We will check the significance of the regression coefficients. If we get any non significant variable in the given multiple regression model, then we will ignore this variable, and we again run the same regression model without deleted explanatory variable.

This multiple regression model will provide the better idea for the prediction of the total revenue of the industry.

Here, we have to study application of decision model by using proper statistical method. For the prediction of the profit or revenue of the business industry, the multiple regression model will be helpful. For this regression model, all significant variables should be considered for getting better results. Here, we want to apply the multiple regression model for the prediction of the revenue of a cloth manufacturing plant. By using this regression model, we will get the idea about the revenue of the plant and it helpful for making better decisions about the manufacturing plant. Here, we will consider the explanatory variables such as number of workers in the plant, average annual salary of the workers, type of machinery (0 for old, 1 for new), quality of product (1: low, 2: medium, 3: high). We will check the significance of the given regression model by using proper statistical methods. Let us see this decision model application in detail.

Data is randomly collected from the available sources such as Google scholar research articles or journal articles. No really collected data is used for this study. Simulated data from internet sources is used and references for data are summarised at the end of this research study. Data for this research study is collected for 19 manufacturing plants. Collection of large sample size data is not possible due to unavailability of direct access to websites of manufacturing plants. So, third party data is used for this research study. For this study, data is collected for the variables such as number of workers, average annual salary in $, type of machinery coded 0 if machine is old and coded 1 if machine is new, quality of product (1: Low, 2: Medium, 3: High), and revenue of the plant.

The use of different statistical tools and techniques is becomes mandatory for analysis of business or industry data. The selection of proper tools and techniques is very important for the analysis of data. For this research study, we have to use simple descriptive statistics and multiple regression analysis. By using the descriptive statistics, we get an idea about the variables involved in the data set. First of all we have to see the descriptive statistics for the variables involved in this research study. Descriptive statistics for the variables involved in this study are summarised as below:

Descriptive Statistics
	N	Minimum	Maximum	Mean	Std. Deviation
Number of workers	19	201.00	498.00	343.0526	103.73442
Avg. Salary Annual ($)	19	40131.00	51117.00	45324.0526	3661.99216
Type of Machinery	19	.00	1.00	.6842	.47757
Quality of product	19	1.00	3.00	2.3684	.59726
Revenue $ (annual)	19	96280125.00	2.56E8	1.5655E8	5.17448E7
Valid N (listwise)	19

Application of Decision Model using Statistical Method

From above table, it is observed that the average number of workers in the plant is observed as 343 with the standard deviation of 103.7 approximately. Average annual salary for the employees in the manufacturing plants is observed as $45324.05 with the standard deviation of $3661.99. The average revenue for the manufacturing plants is given as $156.55 million.

Now, we have to see the multiple regression model for the prediction of response variable or dependent variable revenue for the manufacturing plant based on the independent variables such as number of workers, average annual salary, type of machinery, and quality of product.

Variables Entered/Removedb
Model	Variables Entered	Variables Removed	Method
1	Quality of product, Avg. Salary Annual ($), Number of workers, Type of Machinerya	.	Enter
a. All requested variables entered.
b. Dependent Variable: Revenue $ (annual)

The model summary for this multiple regression model is given as below:

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.998a	.995	.994	4.14247E6
a. Predictors: (Constant), Quality of product, Avg. Salary Annual ($), Number of workers, Type of Machinery

From above table,

Multiple correlation coefficient = R = 0.998

Coefficient of determination = R square = 0.995

The multiple correlation coefficient between the dependent variable and independent variables is given as 0.998, which means there is a strong positive linear relationship exists between the dependent variable revenue of the manufacturing plant and combined linear effect of the independent variables such as number of workers, type of machinery, quality of product, and average annual salary of the workers. The value of R square for this model is given as 0.995, this means, about 99.5% of the variation in the dependent variable revenue is explained by the independent variables such as number of workers, type of machinery, quality of product, and average annual salary of employees.

The ANOVA table for this multiple linear regression model is given as below:

ANOVAb
Model	Sum of Squares	df	Mean Square	F	Sig.
1	Regression	4.796E16	4	1.199E16	698.644	.000a
Residual	2.402E14	14	1.716E13
Total	4.820E16	18
a. Predictors: (Constant), Quality of product, Avg. Salary Annual ($), Number of workers, Type of Machinery
b. Dependent Variable: Revenue $ (annual)

From the above ANOVA table, we get

P-value = 0.00

Level of significance = α = 0.05

P-value < α = 0.05

So, we reject the null H₀

There is sufficient evidence to conclude that the regression model is statistically significant.

There is a significant relationship exists between the response variable and explanatory variables for the given model.

The regression coefficients for the independent variables involved in this multiple linear regression model are summarised as below:

Coefficientsa
Model	Unstandardized Coefficients	Standardized Coefficients	t	Sig.
B	Std. Error	Beta
1	(Constant)	-1.748E8	1.357E7		-12.882	.000
Number of workers	470520.973	9798.029	.943	48.022	.000
Avg. Salary Annual ($)	3686.564	284.994	.261	12.936	.000
Type of Machinery	2032604.872	2246944.951	.019	.905	.381
Quality of product	595999.354	1756537.250	.007	.339	.739
a. Dependent Variable: Revenue $ (annual)

From above table, it is observed that variables number of workers and average annual salary of the employees are statistically as the p-value is given as 0.00. Two variables such as type of machinery and quality of product do not have statistical significance as the corresponding p-values are given as 0.381 and 0.739 respectively.

There would be limitations for this study. This study is not based on the direct data from the industries. Also, sample size for this research study is very small and this could be impact on the final results of the study. There would be possibility of getting biased results. In the actual research study based on large sample size data from primary sources, results may be vary from the results of this study. Results for this research study cannot be generalized for all manufacturing plants in the country. For overcoming these limitations, a large sample size research study is required for getting unbiased estimates.

For this research study of application of decision model, we get the following results:

1. It is observed that the average number of workers in the plant is observed as 343 with the standard deviation of 103.7 approximately. Average annual salary for the employees in the manufacturing plants is observed as $45324.05 with the standard deviation of $3661.99. The average revenue for the manufacturing plants is given as $156.55 million.

1. The multiple correlation coefficient between the dependent variable and independent variables is given as 0.998, which means there is a strong positive linear relationship exists between the dependent variable revenue of the manufacturing plant and independent variables.
2. About 99.5% of the variation in the dependent variable revenue is explained by the independent variables such as number of workers, type of machinery, quality of product, and average annual salary of employees.

1. There is sufficient evidence to conclude that the given regression model is statistically significant and it is useful for further prediction.

1. Two variables such as type of machinery and quality of product do not have statistical significance as the corresponding p-values are given as 0.381 and 0.739 respectively.

Conclusions

From above study, we concluded that the given regression model is statistically significant and it is useful for further prediction. There is a statistically significant relationship exists between the dependent variable and independent variables. Also, there is a limitation for the use of this regression model as it is based on small sample size and two independent variables are not statistically significant.

References

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https://journals.sagepub.com/doi/abs/10.1177/002795018912800104