Assessment 2: Business Analytics Case Study Group Report

Assessment 2: Business Analytics Case Study

Group Report

Student Name: Sai Kumar

Student ID: 4081792 

Student Name: Ali 

Student ID: 4100111 

Word Count: 2958

Executive Summary

This study looks at Woolworths Group's financial success over the past nine years and shows big changes in profits along with steady growth in sales. Profit margins show organisational errors, especially in cost management, even though income stays the same. Correlation research shows that there aren't strong links between profit and sales and a negative link between profit and running expenses. This means that higher costs hurt profitability. The model used doesn't have statistical significance, as shown by regression analysis. This means that it might not be able to accurately predict profit trends. Improvements suggested include bettering how costs are managed, how product is handled, and how customer feedback is used to increase profits. In the future, researchers should look into more factors, such as market trends and how competitors act, to get a better sense of what causes profits to vary.

Table of Figure

Figure 1: Descriptive Statistics 9

Figure 2: Line Plot of Profit and Revenue over Year 10

Figure 3: Line Plot of Long term debt and Equity over Year 10

Figure 4: Column chart of COGS and Operating Expenses 11

Figure 5: Correlation Analysis 11

Figure 6: Regression Analysis 12

Company Background

Woolworths Group Limited possesses one among the biggest stores in Australia. Its main business is a supermarket chain, but it also has other stores in Australia and New Zealand. Coles, Aldi, and IGA are just a few of the big stores that compete with the company. Woolworths is worth about AUD 43.52 billion on the stock market as of August 2024, and as of June 30, 2024, it employs about 201,413 people. The business made AUD 67.92 billion in sales in the last 12 months.

Woolworths' main office is in Bella Vista, New South Wales, which is also where all of its processes are run. The company runs around 1,085 Metro Food Stores and Woolworths supermarkets. About 185 New Zealand Supermarkets work for the company. The company also runs 179 BIG W shops in Australia that buy discounted general goods to sell to customers there. The company also does business online for its main trade units.

Problem Statement

The Woolworths Group's financial information from 2015 to 2024 shows big changes in profits, which points to problems with operations and money management.

While revenue growth has been pretty stable, fluctuating profit margins and rising costs, like running costs and the cost of revenue, show that resources and costs may not be being managed as efficiently as they could be.

The issue is very important for making better financial plans, running operations more smoothly, and making more money overall. Trend analysis and statistical models are two types of business analytics that can be used to find patterns in a company's financial performance. This helps the company figure out why its profits aren't stable and its operations aren't running as smoothly as they could. Looking at this information can also help with coming up with ways to fix problems, make things more stable financially, and guess how things will go in the future. In order for Woolworths to keep making money and stay competitive in the market, it needs to fix these problems by making good decisions based on data.

Literature Review

The retail industry has paid a lot of attention to business analytics as a way to solve practical, financial, and customer-driven problems. Different approaches, like descriptive statistics, prediction analytics, and cost optimisation techniques, are being used to figure out how well a business is doing and help make strategic decisions.

Descriptive analytic is generally applied in order to extract patterns from data. The research applied descriptive analytics to identify time and geographical sales patterns. This was because it assisted the company to come up with better marketing strategies that would help reach certain areas. Foudi’s work from the year 2023 analyzed the use of business analytics in retail industry to improve the costs. They also explored how data could reveal where the supply chain was not optimised and this could be a huge cost saving. They looked at the past cost and compared it with the operating results and eliminated the unnecessary costs making the business more profitable.

Often in shopping, the use of predictive analytics was made in order to make predictions concerning sales and revenues. According to Raizada and Saini in his study in 2021, the machine learning algorithms which were used include linear regression and decision trees whereby other people were asked to predict how much money store shops would make and the results were then compared to the actual data, demographics as well as economic indicators. This work brought out the fact that through proper assumption of sales, organizations can be able to cut down on costs and increase their revenues through proper utilization of their resources and stocks.

Another study that was related was conducted by NGUYEN et al. (2020) and focused on how to select key factors for financial performance analysis. They conducted regression and association analysis to determine which factors affect the money-making potential of the retail industry the most. Regional sales trends. This helped the company make marketing strategies that were more effective in reaching specific areas. Foudy’s research study from 2023 looked at how business analytics was employed to make cost structures better in the retail industry. They looked into how data could show where the supply chain wasn't working as well as it could, which could save a lot of money. Through the review of historical costs relative to operational results they trimmed unneeded expenses and improved the business's profitability.

Frequently retailers utilized predictive analytics to forecast profits and sales. In 2021 Raizada and Saini explored the use of linear regression and decision trees as machine learning methods to forecast retail shop revenues from previous data and economic indicators. This examination revealed that effective predictions about sales can allow firms to minimize costs and boost earnings by effectively allocating their resources and supplies.

The 2020 research by NGUYEN et al. focused on selecting significant elements for analysing business profitability. By applying regression and association strategies they pinpointed the main drivers that affected the profit potential within the retail field. Table. Some of the most important variables that were used to identify the reasons why profits varied included inventory levels, costs of goods sold and running costs.

Many retail companies have come to use business data to track sales. Ban and Keskin (2021) focused on how forecasting and price setting strategies were employed to alter prices in relation to demand elasticity and market competitiveness. Organisations could increase their profit margins if they adopted the use of past prices and customers’ purchase histories to help them alter their prices as they happen.

A study by Salam et al. (2021) used correlation analysis to look at the link between COGS and revenue. The study found that managing supply chain costs and getting rid of operating errors were key to making more money in the very competitive retail industry.

Retailers can use descriptive statistics, prediction analytics, and big data methods to make choices based on data, make the best use of their resources, and make more money. Gawankar et al. The studies we looked at show how business analytics techniques have been used successfully in retail. They can be used as a starting point for more research and use in similar areas.

Data Collection

Using company records, financial data websites, as well as web scraping methods in a planned way to gather information. This process made sure that there was a complete sample that could be analysed.

The data is mainly extracted from the financial information available on the Woolworths Group website. The most recent yearly reports were used to get information about operating plans, success measures, and full financial accounts for the preceding ten years Woolworthsgroup.com.au (2023). Market capitalisation Stock Analysis (2024) can help you get more financial information, such as past stock prices. In 2024, Morningstar gave Woolworths and its peers in-depth analyses and grades.

Combined several smaller datasets from the sources listed to make a full dataset. After collecting this information, I put it in an ordered manner using a spreadsheet program (like Microsoft Excel). This made it easy to see trends over several years and analyse them. They used a web scraping tool to replicate the information from the website in order to make money.

Null values have been taken out of the dataset, and the information is now organised in a way that makes it easier to analyse. The analysis looks promising when the data is cleaned and structured in a table or simpler form.

Analysis and Discussion

Descriptive Statistics

Descriptive analysis is important because it gives a clear picture of Woolworths' financial health. This helps people see trends, figure out how efficient operations are, and make smart choices about how to allocate resources and plan strategically. When managers understand these measures, they can see exactly what needs to be fixed, like keeping costs down and making the best use of goods. This helps the business make more money and last longer.

Figure 1: Descriptive Statistics

The table shows an overview of detailed data for Woolworths's most important financial measures, such as Inventory, Profit, Revenue, and Cost of Revenue. The Mean numbers show average performance, with Revenue at 58650.5 and Profit at 1976.1. This shows a strong base of revenue but a wide range in profitability, as shown by the high Standard Deviation of 2378.03. This shows that profitability varies between readings. Operating costs are also high, averaging $13,976.1 per month, which shows that there may be ways to cut costs. The Skewness values, especially for Profit (1.77), show a positive skew. This means that while most earnings are lower, there are some that are much higher than average, which could affect the profitability of the organisation.

Figure 2: Line Plot of Profit and Revenue over Year

Figure out how Revenue and Profit are connected with line chart. In general, sales have gone up, from $59,001 million in 2015 to $67,922 million in 2024. However, profits have been more unpredictable, going negative in 2016 and reaching their highest point in 2022. Even though income is steadily rising, profit rates aren't always the same. This could mean that it's hard to keep costs down and run the business efficiently. A regular rise in sales without a matching rise in profits could be a sign of inefficiency, while a rise in debt could mean that the business needs to borrow money from outside sources, which could hurt its long-term viability.

Figure 3: Line Plot of Long term debt and Equity over Year

Long-Term Debt vs. Total Equity is shown on the graph. When you look at 2020, you can see that debt went from $2,855 million within 2019 to $14,806 million, which is a big jump. At the same time, wealth went down a lot. Since then, debt has stayed high, and wealth has slowly grown back but is still smaller than it was before 2020. This makes me think that Woolworths possibly taken on more debt to pay for growth or get through tough financial times during these years. By keeping an eye on these measures, you can tell if Woolworths' tactics are helping the company make more money and keep a good mix between debt and stock.

Figure 4: Column chart of COGS and Operating Expenses

Cost of Goods Sold (COGS) and Operating Expenses have both gone up from 2015 to 2024, as shown by the stacked column chart. However, COGS has always made up a bigger part of total expenses. Particularly, each COGS and operating expenses went up a lot after 2020. In 2024, COGS reached its highest level of 49,370 and operating expenses reached their highest level of 16,936. This means that the prices of running the business and getting products are going up. This could be because of things in the market, like inflation, or problems with the supply chain. Keeping these costs down is important for Woolworths to protect its profit margins, especially since the difference between cost of goods sold (COGS) and running expenses has been growing over the past few years. If this problem isn't fixed, it could hurt the company's total profitability. This study helps Woolworths figure out where they need to cut costs and improve efficiency to stay financially healthy.

Correlation Analysis

Figure 5: Correlation Analysis

Correlation analysis is used to find out how strong and which way the links between factors are going. In this case, there is only a weak positive association between profit and revenue (0.17). This means that more revenue doesn't necessarily mean more profit. Profit and operating expenses are somewhat negatively related (-0.28), which means that as operating costs rise, profit usually falls as well. There is a small positive relationship between long-term debt and earnings (0.27), which suggests that Woolworth's could use debt to help the business make more money. Notably, both inventory and total equity have negative relationships with profit (-0.43 and -0.14, respectively), which could mean that stock or capital allocation is not being used as efficiently as it could be.

Cost of Goods Sold (COGS) and credit are not looked at in more detail because COGS has a strong relationship with income (0.99), which means they basically show the same trend. Since goodwill has weak relationships with most factors, it doesn't have a clear effect on short-term practical or financial results. So, focussing on other factors gives us more useful information about how to handle financial results.

Regression Analysis

Figure 6: Regression Analysis

The goal of the regression study was to find out how Woolworth's profit related to things like revenue, cost of revenue, running expenses, long-term loans, total equity, as well as inventory. The R-square value of 63.6% in the model suggests that these variables explain a lot of the variation in profits. However, the negative modified R-square value (-0.09) and high-importance F-value (0.595) show that the model is not statistically significant and may include predictors that are not relevant. All p-values had been above 0.05, which means that none of the factors, including income and cost of revenue, had a big effect on profit. For example, there were weak negative coefficients between income and running costs and positive coefficients between long-term debt and total equity, but these were not significant indicators. Even though the negative link between stockpiles and earnings was greater, it wasn't statistically significant. The study shows that the current model isn't good at predicting profit trends, so it needs to be improved by adding more important factors or taking a bigger sample of the data. This information is very important for Woolworths to understand what makes a business profitable and make better financial decisions.

Recommendation

The issues Woolworths Group have in the fluctuations of profits and in the operation of the company that the following recommendations should be made to help improve the financial position and revenue of the First of all, Woolworths need to work on cost management, which means it should pay attention to its operating expenditure and the costs of sales. This is especially so now that we have inflation and there are hitches in the supply chain.

The following should also be done to check on the financial position of the company and in order to enhance the operations of the company. This enables you to manage your stock better and it will also assist in making proper marketing strategies. It will be even easier to make money if you also include customer measures such as purchase price and customer satisfaction. There are some limitations in this study; for instance, a negative corrected R-squared coefficient and others variables have not included in the study. As these shortcomings reveal, we have to work with a larger sample and a superior model to determine the best predictors of profit.

Future research should also consider weather impact, productivity of the workers and a comparison between Woolworths and its competitors. These are the most important areas that Woolworths can use in order to establish strategies and plans on how it can enhance its decisions for the purpose of improving the company’s competitiveness.

Reference

Ban GY and Keskin NB (2021) 'Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity', Management Science, 67(9): 5549-5568, https://www.researchgate.net/profile/N-Bora-Keskin/publication/317273060_Personalized_Dynamic_Pricing_with_Machine_Learning/links/5ebe5f56458515626ca8601e/Personalized-Dynamic-Pricing-with-Machine-Learning.pdf

Foudi L (2023) 'Leveraging Data Analytics for Improving Financial Performance: A Case Study of the Retail Industry', Theseus, https://www.theseus.fi/bitstream/handle/10024/809512/Foudi_Larbi.pdf?sequence=2

Gawankar SA, Gunasekaran A and Kamble S (2020) 'A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context', International Journal of Production Research, 58(5): 1574-1593, https://www.researchgate.net/profile/Shradha-Gawankar/publication/336091611_A_study_on_investments_in_the_big_data-driven_supply_chain_performance_measures_and_organisational_performance_in_Indian_retail_40_context/links/5d8f901a92851c33e94628ad/A-study-on-investments-in-the-big-data-driven-supply-chain-performance-measures-and-organisational-performance-in-Indian-retail-40-context.pdf

Morningstar, I. (2024). WOW - Woolworths Group Ltd Financials | Morningstar. [online] Morningstar, accessed 26 September 2024. https://www.morningstar.com/stocks/xasx/wow/financials

Nguyen PH, Tsai JF, Nguyen VT, Vu DD and Dao TK (2020) 'A decision support model for financial performance evaluation of listed companies in the Vietnamese retailing industry', The Journal of Asian Finance, Economics and Business, 7(12): 1005-1015, https://koreascience.kr/article/JAKO202034651879511.pdf

Raizada S and Saini JR (2021) 'Comparative analysis of supervised machine learning techniques for sales forecasting', International Journal of Advanced Computer Science and Applications, 12(11): 102-110, https://www.academia.edu/download/86566701/Paper_12-Comparative_Analysis_of_Supervised_Machine_Learning_Techniques.pdf

Salam KN, Wulansari R and Harsono P (2021) 'Promotion costs analysis to increase volume sales in the convection companies', International Journal of Science, Technology & Management, 2(5): 1542-1551, https://www.ijstm.inarah.co.id/index.php/ijstm/article/download/300/327

Stock Analysis (2024) Woolworths Group Limited (ASX: WOW) Statistics & Valuation Metrics - Stock Analysis, accessed 25 September 2024. https://stockanalysis.com/quote/asx/WOW/statistics/

Woolworthsgroup.com.au. (2023). Reports and Data, accessed 25 September 2024. https://www.woolworthsgroup.com.au/au/en/investors/our-performance/reports.html

Statement of Collaboration

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