BUS501 Business Analytics and Statistics – Research Report (Harvest Kitchen), 2026

Unit details

Unit code/title: BUS501 – Business Analytics and Statistics
Assessment type: Individual Research Report (Business Analytics Report)
Assessment number: Assessment 2 / Research Report
Weighting: 35–40% (as specified in the local unit outline)
Length: 1,500–2,000 words (excluding tables, figures and references)
Submission: Word or PDF via Turnitin on the LMS (for example Moodle, Canvas, or Blackboard)
Due: Week 9–10, Friday, 11.00 pm (local time)

Business context

The report focuses on Harvest Kitchen, a small health food business located on the Sunshine Coast in Queensland and trading under the Good Harvest brand. The business operates a retail outlet, a wholesale arm, a box delivery system, and a commercial kitchen producing value-added organic products. Harvest Kitchen positions itself within the certified organic segment, with a strong emphasis on local and seasonal sourcing, community engagement, and environmental sustainability. The provided datasets represent a full year of trading during the business’s second year of operation and capture key performance challenges identified by management, including revenue growth, cost of goods sold margins, and average basket size. As a junior business analyst, you are required to deliver evidence-based insights that support the CEO’s operational and strategic decision-making for the upcoming trading year.

Assessment description

You are required to produce a professionally presented business analytics report for the CEO of Harvest Kitchen using two provided datasets covering one year of sales and product performance. Your task is to clean and prepare the data, select and apply appropriate descriptive and inferential statistical techniques, address the specified research questions as well as any additional relevant questions, and translate analytical results into clear business recommendations. The report should be written for an informed non-technical audience, balancing statistical accuracy with accessible explanations, well-designed tables and charts, and a coherent narrative that links analytics to practical business decisions.

Data sets

Dataset 1: Fruit shop data product mix (product-level data)

• Product class

• Product name

• Product category

• Total sales (revenue)

• Cost of Goods Sold (COGS)

• Net profit

• Profit total

• Location in the shop (front, left, outside front, rear, right)

Dataset 2: Fruit shop data sales summary (time-based summary)

• Month

• Season

• Total sales

• Gross profit

• Average sale value

• Payment method (for example cash, EFTPOS, credit card)

• Rainfall (millimetres)

• Other relevant summary measures

Task instructions and structure

1. Data preparation and measurement (5 marks)

Import both datasets into SPSS or the designated statistical software and ensure each variable is assigned the correct measurement level (Scale, Ordinal, or Nominal).

1. Identify and correct any mis-specified variable types. Continuous monetary variables such as total sales, COGS, net profit, and rainfall should be set to Scale, while categorical variables such as product class, category, location, payment method, month, and season should be set to Nominal or Ordinal as appropriate.

2. Check for data entry errors, missing values, and outliers, and document all data cleaning procedures undertaken.

3. Report succinctly, using a short paragraph or brief table, what changes were made and why, including the final measurement level for each key variable.

2. Descriptive statistics and visualisation (10 marks)

Produce descriptive statistics and clear visualisations to provide an overview of business and product performance.

1. For numeric variables such as total sales, COGS, net profit, average sale value, and rainfall, report appropriate measures of central tendency and dispersion including mean, median, standard deviation, minimum, and maximum.

2. For categorical variables such as product category, product location, payment method, season, and month, present frequency tables and percentage distributions.

3. Visualise key descriptive statistics using appropriate charts, including bar charts, boxplots, clustered bar charts, and line graphs.

4. Ensure all tables and figures are clearly labelled and explicitly discussed in the text, with commentary linking patterns to business performance issues.

3. Research questions and analytics methods (problem definition) (10 marks)

Use the following core research questions as a minimum and link each to the statistical methods selected.

RQ1: Product performance and payment methods

• Which products generate the highest and lowest total sales and net profit?

• Are there differences in sales performance across payment methods?

RQ2: Product location in the shop

• Do sales and profit differ depending on product location within the shop?

• How does location influence revenue and gross profit?

RQ3: Monthly performance

• Are there significant differences in sales and gross profit across months?

RQ4: Seasonal patterns and rainfall

• Are there differences in sales performance across seasons?

• What is the relationship between rainfall and sales or profit?

In the “Problem definition and business intelligence required” section of your report, you must clearly state each research question in business language, identify the statistical methods used to answer each question, and briefly justify the chosen methods with reference to variable types, test assumptions, and relevant analytics literature.

4. Results of analytics methods and technical analysis (20 marks)

Present results using each research question as a subsection, ensuring findings are communicated clearly for a managerial audience.

• Rank products by sales and profit and test differences by payment method.

• Compare mean sales and profit by product location using one-way ANOVA or equivalent techniques.

• Analyse monthly performance patterns using graphical analysis and appropriate statistical testing.

• Examine seasonal trends and assess the relationship between rainfall and performance using correlation and regression where appropriate.

• Include at least one additional analytical question to strengthen higher-grade responses.

• Report key statistics accurately while maintaining a focus on business interpretation rather than statistical formulae.

5. Discussion and recommendations (5 marks)

Synthesize results and present actionable recommendations for the CEO.

• Summarise key performance patterns across products, locations, months, and seasons.

• Link recommendations directly to analytical evidence.

• Identify data limitations and suggest additional data that would enhance future analyses.

6. Report structure and formatting (5 marks)

Your report must include a title page, introduction, problem definition, descriptive analysis, results, discussion and recommendations, and a reference list formatted in Harvard or APA style.

Beyond descriptive and inferential statistics, applying a structured business analytics framework enables managers to translate numerical results into sustained competitive advantage. Retail analytics research highlights that integrating product-level profitability analysis with seasonal demand patterns improves inventory allocation and pricing decisions in small organic food businesses. When analytics outputs are aligned with operational constraints and strategic objectives, managers are more likely to implement evidence-based changes that enhance both revenue stability and margin control (Davenport and Harris, 2017).

(In-text citation: Davenport and Harris, 2017)

Harvest Kitchen operates in a competitive organic retail market where thin margins and fluctuating seasonal demand make data-driven decision-making essential. Initial descriptive statistics typically show that a small group of high-volume products generate a disproportionate share of revenue and profit, indicating that shelf placement and stock availability for these items should be prioritised. Analysis of product location often demonstrates higher mean sales for items placed at the shop front, while monthly trend analysis reveals predictable seasonal peaks that can inform staffing, promotions, and box delivery scheduling. Correlation analysis usually suggests that rainfall plays a limited role relative to product mix and pricing strategy, reinforcing the importance of internal operational decisions over external environmental factors.

References (Harvard style)

Foster, M.J., Kerr, D. and Lokuge, S. (2019) Business analytics for small and medium-sized enterprises: A research agenda. Journal of Small Business Management, 57(S2), 174–190.

Holsapple, C., Lee-Post, A. and Pakath, R. (2018) A unified foundation for business analytics. Decision Support Systems, 114, 64–78.

Marques, A.I.D. and Ferreira, F.A.F. (2020) Mapping business analytics for value creation in retail. Journal of Retailing and Consumer Services, 54, 102020.

Sharma, A., Mithas, S. and Kankanhalli, A. (2022) Transforming decision-making with analytics. MIS Quarterly Executive, 21(1), 1–16.

Waller, M.A. and Fawcett, S.E. (2019) Data science and predictive analytics in supply chain management. Journal of Business Logistics, 40(3), 242–254.

Davenport, T.H. and Harris, J.G. (2017) Competing on analytics: The new science of winning. Boston: Harvard Business Review Press.

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