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DATA ANALYSIS AND CONCLUSIONS FROM QANTAS AIRWAYS 4

DATA ANALYSIS AND CONCLUSIONS FROM QANTAS AIRWAYS

Data analysis and conclusion from Qantas Airways Company

Introduction

Data breakdown has been used from time to time by the management of all organizations as a tool for strategic management. Analyzing data is essential in business organizations as it helps in understanding the organizational challenges and explores data in meaningful ways. It involves cleaning, transforming, and modeling data that helps management and staff make informed decisions (Zou et al. 2020). Information provided below entails analysis of Qantas Airways Company income projections. It is an analysis of revenue, cost, and profit for the airline.

Data analysis

There are a table and a chart in the revenue worksheet labeled ‘exponential smoothing of total yearly revenue.’ The data presented shows revenues earned by Qantas Airways from 2018 to 2019 and revenue projections for three years to come. Revenue for the airline is decreasing from 2018, as illustrated by the blue column, representing revenues. This may be a result of low sales brought about by poor customer handling services, charging higher prices or increased competition among airline companies in Australia. The revenue is also projected to decrease in the future, as shown by the orange columns representing forecasts which are decreasing as from 2019 to 2022. This may be brought about by external factors, and the organization should look for revenue creating strategies to increase profit (Fitri et al. 2020).

Expenses worksheet has a table and a graph titled ‘expenses forecast.’ Data presented show the cost of running the business for two consecutive years and forecast future cost for the next three years. Expenses are also decreasing from 2018 to 2019, as illustrated by the blue column representing expenses. This may be brought about by discounts received and reduced the costs of running the business. It is expected that costs will reduce in the future, as illustrated by the orange bars representing forecasts. The organization should look into more ways of cutting expenses to ensure quality service production by the airline (Fitri et al., 2020).

Profit worksheet has a profit forecast table and a graph ‘net revenue fewer expenses.’ The airline is making a profit as indicated by the blue column representing ‘profit.’ Profit is also expected to increase in the future, as highlighted by the orange columns in the line graph. This will be brought about by economies of scale (Fitri et al., 2020).

All charts in the worksheets have lower confidence bound and upper confidence bound in their projections. These confidence intervals propose a range of possible values for revenues, costs, and profit for the organization. The intervals have an associated confidence level that the actual value is in the proposed range (Zhang et al., 2020). The real value is precisely in the range between the upper and lower confidence bound with the orange column in each column bar. The upper and lower confidence level are colored purple and grey, respectively.

Conclusion

According to the data from the table, the airways are expected to make more profit if they apply cost-effective approaches and serve their clients in the best way. If the organization uses the Enterprise Resource planning, the revenue levels will increase, and their cost levels will go low as the system will ensure productivity and quality customer service (Aggarwal et al. 2020). The main objective is to make sales, and it will do so if it applies this system, which will ensure that all programs run smoothly and will make the Airways have a higher competitive edge than other airlines internationally.

References

Zou, X., Chen, K., Zou, J., Han, P., Hao, J., & Han, Z. (2020). Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection. Frontiers of medicine, 1-8.

Fitri, E., Darmansyah, A., & Damayanti, S. M. (2020). Bankruptcy Prediction Analysis of PT Garuda Indonesia Compared to Four Airlines Companies in Asia. KnE Social Sciences, 1148-1161.

Aggarwal, D., Saxena, D. K., Back, T., & Emmerich, M. (2020). Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method. arXiv preprint arXiv:2003.03792.

Zhang, B., Zuo, H., Huang, Z., Tan, J., & Zuo, Q. (2020). Endpoint forecast of the different diesel-biodiesel soot filtration process in diesel particulate filters considering ash deposition. Fuel, 272, 117678.