Problem statement
Discusss about the Study Of Monthly Turnover Rate Estimates In Retail Industries, Australia.
Certain publications are available on the internet that offers deeper market analysis using the retail turnover trade. Research shows that most economists and statisticians compile monthly reports on turnover estimates in different parts of the world. These turnover estimates are very important in interpreting the market performance in a given country (Lal & Siahpush, 2009, p.405). Monthly retail turnover estimates are presented in terms of the current price. Turnover may be defined as the retail and wholesale sale or commission collected from the agency activities such as selling lottery tickets (Jensen, 2011, np). The ABS uses economic models in modeling their economic data. This dissertation will also formulate some economic model that will model the data provided. The model will show some structural relationship between certain variables to be discussed in this dissertation.
This survey uses economic factors and also some industry groups and subgroups in exploring the retail trade statistics. This improves the coverage and also the quality of the turnover estimates as each of the classes/groups provides a reliable information that is used in the market analysis of these factors. This survey is conducted on a monthly basis as to ensure no breaches in the data intervals. The data that was used in this dissertation is a secondary data published on the ABS website and therefore has been used in this project to offer a deeper understanding of retaining turnover determinants. A generalized methodology is used also in this paper in gaining an insight into the turnover market trend. Many enterprises provide their turnover reports on monthly basis accompanied by data collected during the survey. Then this information is used in decision making by qualified economists and statisticians.
Problem statement
The retail industry is a competitive industry in our economy today. Retailers and dealers in this industry are concerned with making high turnover rates from their businesses. Though many retail industries have failed in meeting this goal (Rahman et al. 2010, p.156). The trends of the turnover rates in a calendar year play a big role in providing an insight to retailers on peak seasons and the off-peak season that help them adjust their operations appropriately depending on the expected retail sales (Kolias, Dimelis & Filios, 2011, p.143). Therefore, the study of these seasonal patterns in retail industries is an important aspect in evaluating the performance of any retail industry in terms of the retail sales it makes in a given month or a specific calendar year.
Objectives of the study
Objectives of the study
The overall objective of this study is to explore whether there exist a relationship in the monthly turnover estimates of the different retail industries discussed in this dissertation. The study is also used to provide a reliable and trusted economic information that is crucial for decision making in trade industry specifically the retail turnover trade. Therefore the study has focused on data reliability in making informed decisions by the government agencies and also research agencies. This economic data is modeled to study the trends in the retail industry. The study also has explored the impacts of certain economic factors on the retail trade. Generally, the monthly report compiled from this dissertation shows how certain retail industries vary in terms of their turnover estimates. These retail industries discussed in this paper include; Food retailing, supermarket and grocery stores, Liquor retailing, households’ goods retailing, electrical and electronic goods retailing and others. Therefore the goal of this project is to explore monthly turnover estimates of these different retail industries.
Hypothesis testing
Hypothesis testing shows how the outlined market variables will influence the market productivity. Therefore, it shows some relationship between the predictor variables and the response variable. Our hypothesis testing in this study is to investigate whether there is a relationship in the monthly turnover estimates of different retail industries provided in the data set. Therefore;
The overall study, therefore, explores whether there is a general relationship in the monthly turnover estimates of the different retail industries in the data set provided. The hypothesis targets the overall goal of the study.
Retailers are interested in higher stock turnover rates in their businesses. For instance, supermarket retailing industry will do well where the retail turnover rates are very high (Alexander & Doherty, 2009, np). Some factors may also influence the rate of retail sales. These factors may be economical, social, political or environmental (Tian-Foreman, 2009, p.356). Further investigations in this field have been done to put into account these confounding variables that influence the behavior of the retail turnover rates (Australia, 2011, p.24). Customer relationship with the retailer in a specific industry is also an important factor that may influence the turnover rates of a certain retail industry (Jefferson & Preston, 2010, p.335). Therefore good customer relations is a crucial factor in determining the retail turnover rates of any retail industry.
In the modern world, technology has improvised the strategies of making retail sales through online forums that promote retail trade (Lynch et al. 2011, p.277). Through these online forums, many businesses have grown into bigger firms thus extending their sizes and also becoming higher-profit makers (Baltagi, 2008, np). Online retail selling has also reduced the cost of production in both retail and wholesale industries as now most of the labor work is done by fewer individuals who operate with business machines (Gaur & Kesavan, 2015, np). Generally, we can conclude that technology has played a positive role in promoting many retail industries around the globe (Davis, Smith & Marsden, 2007, np).
Hypothesis testing
Some market theories have also been developed to study market forces that may influence retail turnover rates in retail industries (Lee, Zhou & Hsu, 2015, p.35). These market forces are said to play a big transitional role to business performance (Booth & Hamer, 2007, p.289). Researchers, therefore have invested in making further researcher in providing a proper conclusion to these factors that influence the retail turnover rates (Capkun, Hameri & Weiss, 2009, p.789). Study of the retail turnover rates will provide an insight into different industries in formulating their business policies.
As indicated earlier, these turnover estimate reports are produced on monthly basis (Choudhary & Tripathi, 2012. P.43). The survey has been conducted primarily by use of data collection techniques such as use questionnaires, telephone interviews, direct interviews or mailing some questionnaires to businesses (Jefferson & Preston, 2010, p.335). These businesses that are involved in the survey are selected randomly based on their size and also the industry. The annualized turnover is used to evaluate the size of the business. Some stratification technique is also used in grouping different industries into their specific classes (Eroglu & Hofer, 2011, p.356). A graphical methodology is used to show the trend in the monthly turnover estimates of different industries. Stratified sampling technique is employed when grouping the industries into classes they belong to. Therefore, different industries are viewed as strata.
Stratification has an advantage over other sampling techniques; for instance, different groups are put into strata with homogeneous characteristics (McLachlan, 2013, np). Several categories (strata) of industries were used in this research paper. Each industry had a corresponding monthly turnover value that was analyzed using statistical software and Microsoft Excel. The data analysis process involved outputting of statistical charts and graphs. These graphs and charts were used in making some conclusions based on their shape and the trend displayed.
This section discusses the findings of the research project. The data analysis focuses on volume of retail sales (retail turnover) in different times of the year for retail industries in Australia which include: Food retailing, supermarket and grocery stores, liquor retailing, household goods retailing, furniture floor coverings, houseware and textile goods retailing and electrical and electronic goods retailing. Graphs and charts have been used to show the trend in the retail sales at different points in time of the year.
Distribution of turnover estimate in November -2017
Research findings of the study showed that in the month of November -2017, supermarket and grocery stores industry recorded the highest turnover estimates while liquor industry recorded the least turnover estimates in that specific month of the year. The results of the findings of the study were as shown in figure 1.2 below
Literature Review
Monthly turnover estimates in each retail industry
The findings of the research showed how monthly turnover estimates were distributed in each of the retail industry under the study. Food retailing, supermarket and grocery stores were the industries that recorded higher turnover estimates in the past six months of the study while furniture, floor covering, houseware and textile goods retailing and liquor retailing recorded the least turnover estimates in the past six months. The findings also show that retail sales were higher in the month of December -2017 in all the various types of retail industry.
Trend of the monthly turnover estimates
The findings of the research investigated trends in turnover estimates of each industry category. The volume of retail sales seemed to shoot up in the month of December -2017 in all retail industries discussed in this study (Choi & Varian, 2012, p.2). And generally, retail turnover was very low in the month of January-2018. The difference in the two occasions could be due to some economic or environmental factors that have not been exhausted by this research project. The trend of the turnover estimate was as shown in figure 1.4 below
we can say that turnover estimates for each of the industry categories had certain pertain during each of the months of the year. For instance figure 1.5 above shows variation of the turnover estimates in every month. Using our hypothesis testing, that was testing whether a relationship exists in the monthly turnover estimates. From the findings, it seems that the monthly turnover estimates were independent though there could be some variation that could show dependency due to other that were not explored by this study. Therefore, the data could only be modeled through graphing to investigate the effect of trend and seasonal adjustments in the data set.
Conclusions
The datatype of the retail turnover rate estimate was measured in terms of current prices ($ million). The study explored the behavior of the turnover estimates of different industries for a period of six consecutive months. This report shows that the retail sales increased in the month of December-2017 in all the types of retail industries. These estimates reflect on how the retail sales tend to behave in certain periods of the year depending on the type of the retail industry.
The ABS retail small scale (retail) trade include the retail industries discussed in above section. However, there are other retail industries not explored in the study such as online retail sales which also offer a significant role in the study of retail turnover trade analysis. The survey only chose random samples of the retail industries to study the trend of the quarterly retail sales in a year. This dissertation, therefore, brings a clear insight of the retail sales in Australia economy and how they trend at different times of the year. Further research in this field is also needed to expand the knowledge on the behavior of retail sales in different parts of the year and technical methods for statistical analysis also need to be reviewed to capture inner details of this data phenomenon.
Experimental Techniques And Methods
References
Alexander, N. and Doherty, A.M., 2009. International Retailing. Oxford University Press.
Australia, S.W., 2011. Compendium of workers’ compensation statistics Australia 2008-09. Canberra: Safe Work Australia, 24.
Baltagi, B., 2008. Econometric analysis of panel data. John Wiley & Sons.
Booth, S. and Hamer, K., 2007. Labor turnover in the retail industry: Predicting the role of individual, organizational and environmental factors. International Journal of Retail & Distribution Management, 35(4), pp.289-307.
Capkun, V., Hameri, A.P. and Weiss, L.A., 2009. On the relationship between inventory and financial performance in manufacturing companies. International Journal of Operations & Production Management, 29(8), pp.789-806.
Choi, H. and Varian, H., 2012. Predicting the present with Google Trends. Economic Record, 88(s1), pp.2-9.
Choudhary, H. and Tripathi, G., 2012. An analysis of inventory turnover and its impact on financial performance in Indian organized retail industry. Journal of services research, 12(1), p.43.
Davis, J.A., Smith, T.W. and Marsden, P.V., 2007. General Social Surveys, 1972-2006 [Cumulative File]. Inter-university Consortium for Political and Social Research
Eroglu, C. and Hofer, C., 2011. Lean, leaner, too lean? The inventory-performance link revisited. Journal of Operations Management, 29(4), pp.356-369.
Gaur, V. and Kesavan, S., 2015. The effects of firm size and sales growth rate on inventory turnover performance in the US retail sector. In Retail Supply Chain Management (pp. 25-52). Springer, Boston, MA.
Jefferson, T. and Preston, A., 2010. Labour markets and wages in Australia in 2009. Journal of Industrial Relations, 52(3), pp.335-354.
Jefferson, T. and Preston, A., 2010. Labour markets and wages in Australia in 2009. Journal of Industrial Relations, 52(3), pp.335-354.
Jensen, J.B., 2011. Global trade in services: fear, facts, and offshoring. Washington, DC: Peterson Institute for International Economics.
Kolias, G.D., Dimelis, S.P. and Filios, V.P., 2011. An empirical analysis of inventory turnover behavior in Greek retail sector: 2000–2005. International Journal of Production Economics, 133(1), pp.143-153.
Lal, A. and Siahpush, M., 2009. The effect of smoke-free policies on revenue in bars in Tasmania, Australia. Tobacco control, 18(5), pp.405-408.
Lee, H.H., Zhou, J. and Hsu, P.H., 2015. The role of innovation in inventory turnover performance. Decision Support Systems, 76, pp.35-44.
Lynch, S., Price, R., Pyman, A. and Bailey, J., 2011. 14 Representing and Organizing Retail Workers: A Comparative Study of the UK and Australia. Retail work, p.277.
McLachlan, R., 2013. Deep and Persistent Disadvantage in Australia-Productivity Commission Staff Working Paper.
Rahman, A., Afza, T., Qayyum, A. and Bodla, M.A., 2010. Working capital management and corporate performance of manufacturing sector in Pakistan. International Research Journal of Finance and Economics, 47(1), pp.156-169.
Tian-Foreman, W., 2009. Job satisfaction and turnover in the Chinese retail industry. Chinese Management Studies, 3(4), pp.356-378.
Order an Essay Now & Get These Features For Free:
Turnitin Report
Formatting
Title Page
Citation
Outline
