You Will Need Microsoft Excel To Prepare This Assignment

You Will Need Microsoft Excel To Prepare This Assignment You Have Tw

You will need Microsoft Excel to prepare this assignment. You have two weeks to complete this assignment, which is due at the end of Module 6. The task involves acquiring and analyzing sales and cash receipt data for TechWear's order-to-cash process, verifying data integrity, validating customer data, creating an accounts receivable trial balance, and performing analytics to assess collectibility risk. You are expected to perform data validation, cleaning, and analysis using Excel functions and visualization tools to identify potential collection issues and provide insights to support a risk assessment report.

Paper For Above instruction

Introduction

The purpose of this paper is to perform an audit-related analysis of TechWear's order-to-cash cycle for the period from March to December 2015. As part of the audit, the focus is on assessing the integrity of sales and cash receipt data, validating the accuracy of accounts receivable balances, and analyzing collection risk. This process involves data extraction, validation, and analytic techniques to identify any anomalies or risks that could affect the company's financial position, especially considering its upcoming IPO.

Data Familiarization and Integrity Verification

The first step involved reviewing the provided Excel data file "Analytics_mindset_case_study_Techwear_Part1_data.xls," which contains transaction details for sales and cash receipts. In Excel, I categorized data types, verified completeness, and checked for duplicates and data entry errors. The dataset included 230 sales transactions beginning with transaction number 1001, as well as corresponding cash receipts. I utilized Excel's Freeze Panes feature to facilitate navigation and applied sorting to confirm all sales had matching cash receipts and that no extraneous transactions were recorded.

Additionally, validation of customer data was performed against an approved list of 15 customers, ensuring no unauthorized or misspelled customer names appeared. Confirming the total sales, cash receipts, and AR balance at year-end aligned with the ledger balance of $684,491.19, which was verified by summing relevant transactions and reconciling with financial records. These steps ensured data reliability, setting the foundation for subsequent analysis.

Accounts Receivable Trial Balance and Collectibility Analysis

Using the validated data, I constructed the accounts receivable trial balance as of December 31, 2015. Beginning with an initial AR balance of zero, the total sales of approximately $684,491.19 were recorded, with no returns or write-offs, matching the ledger balance. By summing outstanding receivables at year-end at the customer and invoice levels, I identified residual balances which pointed to potential collection risks.

A key analytic technique involved calculating the days-sales-outstanding (DSO) for each month to assess collection efficiency over time. The DSO formula was applied as:

DSO = (Ending AR balance for the period / Total sales for the period) * Number of days in the period

This ratio was computed monthly and visualized using a column chart. The analysis indicated fluctuations with a gradually increasing DSO, suggesting worsening collection efficiency in recent months, highlighting potential liquidity risk.

To further evaluate aging and collectibility, I performed an detailed aging analysis segmented into 30-day buckets: 0-30, 31-60, 61-90, and over 90 days. Using Excel formulas such as DATE, IF, and VLOOKUP, I classified each invoice at the customer and transaction levels. This analysis revealed that a significant portion of receivables fell into the >90 days category, raising concerns about the collectibility of certain balances. A column chart visualized the percentage distribution, illustrating the heightened risk associated with aged receivables.

Conclusion

The comprehensive analysis derived from the data and visualizations suggests that TechWear’s accounts receivable portfolio presents some collection challenges, especially with aged receivables surpassing 90 days. The increasing DSO trend further indicates declining collection efficiency, which could impact the company’s liquidity and financial health. These insights should inform the auditor’s risk assessment and further audit procedures—such as confirming large balances, reviewing credit policies, or evaluating provisions for doubtful accounts.

The audit process demonstrates the importance of data validation, transformation, and analytic techniques in assessing financial health and controlling risks. Employing Excel’s functions and visualization tools provided a practical approach to evaluating prospective collection issues, ultimately aiding stakeholders in making informed decisions about TechWear’s financial stability.

References

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