Burlingham Bee Assignment Review Case 83

Burlingham Bee Assignmentreviewcase 83burlingham Beesp243 245compos

Using analytical procedures as substantive tests, develop expectations for revenue accounts, evaluate differences between expected and reported balances, and consider audit planning implications. Focus on factors that lead to precise expectations and how to assess the reasonableness of reported revenue.

Sample Paper For Above instruction

Introduction

Analytical procedures are vital in audits for understanding client operations, identifying risks, and substantiating account balances. When used as substantive tests, these procedures can enhance audit efficiency and effectiveness by providing reliable evidence for revenue assertions. The case of Burlingham Bees illustrates the application of analytical procedures to revenue accounts, emphasizing their role in audit planning and risk assessment.

Development of Expectations for Revenue Accounts

Expectations for Burlingham Bees’ revenue accounts should be based on multiple factors, including historical trends, industry benchmarks, and client-specific data. Utilizing disaggregated data such as ticket sales by game type, promotional impacts, and attendance patterns enhances expectation accuracy. For instance, analyzing revenue correlations with attendance fluctuations and promotional events adds precision, reducing reliance on oversimplified aggregate analyses.

Specifically, ticket revenues are affected by variables like game day, day of the week, promotion days, and special events. Given that attendance increases by approximately 15% during promotional days, expectations must incorporate these effects. Similarly, differences between weekday and weekend attendances, adjusted by historical averages, help generate a more refined estimate. Recognizing that ticket prices increased by 9% from 2013 to 2014 also influences revenue expectations, as comparable increases in revenue can be projected under stable sales mix conditions.

Furthermore, developing expectations involves calculating anticipated ticket sales volumes by category—club, box, and general seats—and adjusting for their respective sales mixes and attendance patterns. For example, if the average attendance per game is known and the typical sales mix is stable, expected revenue can be calculated by multiplying expected attendance for each game type by the respective ticket price and sales proportion. The expected revenue across all games then provides a benchmark against reported figures.

Advantages of Using Disaggregated Data

Using disaggregated data allows auditors to identify specific areas where actual results deviate from expectations, providing more detailed insights into possible misstatements. Granular data enables auditors to consider variations related to game types, days, promotions, and ticket categories, improving the likelihood of detecting anomalies. Such detailed analysis increases the sensitivity of analytical procedures, making them a more effective tool for substantive testing.

Moreover, disaggregated expectations improve audit efficiency by focusing substantive testing on higher-risk areas. Instead of broadly testing total revenue, auditors can target specific segments such as promotional days or weekend games, where larger deviations are more plausible. This targeted approach enhances audit effectiveness and can result in time and cost savings, as unnecessary detailed testing of lower-risk areas is minimized.

Evaluation of Analytical Procedures and Audit Planning

Implementing analytical procedures alters the audit plan by shifting some substantive evidence collection from detailed testing to expectation-based analysis. This approach allows auditors to identify discrepancies early, prioritize audit efforts, and design targeted audit procedures for areas of concern. Compared to prior years’ plans, which may have relied more heavily on extensive manual testing, the current plan emphasizes analytical review methods, potentially reducing the scope of detailed discrepancies testing.

Regarding the suitability of analytical procedures for Burlingham Bees’ audit, these methods are appropriate given the stable sales mix, predictable attendance patterns, and the availability of independent ticket count data. Because the expected relationships between attendance and revenue are well-understood and supported by historical trends and external data, analytical procedures can serve as a reliable, cost-effective substantive evidence source.

Assessing Reasonableness of Reported Revenue

The reported ticket revenue must be within an acceptable range of the expectation, considering inherent and control risks, as well as sampling variability. A common benchmark is that any discrepancy within 5% of the expectation may be deemed reasonable, provided no other indications of misstatement or control deficiencies exist.

Should reported revenue fall outside this range, auditors must investigate further, potentially performing detailed tests or examining additional data sources. If the discrepancy becomes significant—say, more than 10%—it warrants serious consideration of potential misstatements, requiring inquiries or substantive procedures to confirm accuracy.

In conclusion, combining detailed analytical procedures with professional judgment enhances audit quality. Expectations built on disaggregated data, such as attendance, promotional impact, and pricing trends, allow for precise assessments of revenue fairness. Evaluating deviations from these expectations facilitates effective risk identification and audit planning, ultimately supporting the issuance of a fair and reliable audit opinion.

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