For information and reference only – not to be submitted as your own work. This is a model extract written to show structure, reasoning and calculation layout for the ACCT433 Business Case Document. All cost and benefit figures are illustrative assumptions built from the supplied interview scenario. They are not vendor quotes or real organisational data. Your submission must be your own work, with your own assumptions, and must follow the SMU Code of Academic Integrity and the brief’s AI-use declaration.
Brief: ACCT433 RPA business case document brief. Guide: how to write an RPA business case with ROI and payback.
1. Executive Summary
The Accounts Receivable (AR) team produces past-due customer lists on request for around 200 Account Managers (AMs), who then write individual reminder emails to customers. The process relies on a Xero Aged Receivables export, a manually maintained Excel Customer Master List and name-based matching with no customer ID. On the illustrative volumes in this case it consumes about 6,480 staff hours a year and produces avoidable errors such as wrong customers, wrong amounts and wrong email addresses.
This business case requests approval for a 12-month RPA pilot in which a software robot exports the aged receivables report, matches customers to the master list, filters past-due balances above zero, routes exceptions to an AR queue and drafts reminder emails for AM approval. On conservative assumptions the pilot returns an estimated three-year ROI of 125% with payback in about 11 months from project start. Vision statement: “Every overdue customer receives an accurate, timely reminder approved by the person who owns the relationship – with no manual list-building.”
2. Current State Analysis
2.1 How the process operates
Two roles carry the work. The AR Clerk builds the overdue list; the AM decides who to contact and writes the email. The CRM is not used – the Excel master list is the reference point, which is the root of most errors. This is a structured, rule-based, high-volume task working across several desktop applications, which is the profile van der Aalst et al. (2018) describe as suited to RPA’s “outside-in” approach of operating on existing user interfaces. Early adopters in accounting likewise targeted structured, repetitive, rule-based tasks with digital inputs (Kokina & Blanchette, 2019).
2.2 As-Is process map (pain points flagged)
In a full submission this table would be redrawn as a swimlane diagram (AM, AR Clerk, systems) with the pain point numbers marked on the boxes.
| Step | Actor / system | Activity | Pain point |
|---|---|---|---|
| 1 | AM / email | Emails AR asking for overdue list (≈2× a month, minimum) | PP1 Unscheduled, ad hoc trigger – 400+ requests a month queue up on one team |
| 2 | AR Clerk / Xero | Runs Aged Receivables (“AR Summary Report”) at month-end date | – |
| 3 | AR Clerk / Excel | Exports report to Excel (customer name, past-due balance) | – |
| 4 | AR Clerk / Excel | Opens Customer Master List in shared folder | PP2 Master list maintained by hand; CRM not used; data may be stale |
| 5 | AR Clerk / Excel | Matches report rows to master list by customer name | PP3 No customer ID – similar names, spelling mismatches and duplicates cause wrong matches and missed rows |
| 6 | AR Clerk | Filters customers with past-due balance above 0 for that AM | PP4 Manual filter – rows missed, wrong amounts copied |
| 7 | AR Clerk | Sets aside unmatched customers; queries duplicates with AM | PP5 Exceptions handled informally with no log or audit trail |
| 8 | AR Clerk / email | Builds output file (name, email, balance, AM) and replies to AM (≈10 min per request) | PP6 Repetitive copy-paste work |
| 9 | AM | Resolves blank emails; decides whether to send each reminder | Judgement step – keep human |
| 10 | AM / Gmail | Writes one email per customer from template, 5–8 min each including checks | PP7 Slow; template name left unchanged; wrong email; no invoice breakdown |
2.3 Current-state volumes and time (illustrative)
| Ref | Driver | Value | Source |
|---|---|---|---|
| A1 | Account Managers | 200 | Interview |
| A2 | Requests per AM per month | 2 (minimum) | Interview |
| A3 | Requests per year (A1 × A2 × 12) | 4,800 | Calculated |
| A4 | AR time per request | 10 min | Interview |
| A5 | Past-due customers per request (20% of ~50) | 10 | Illustrative assumption |
| A6 | AM time per reminder email (midpoint of 5–8 min) | 6.5 min | Interview (midpoint) |
| A7 | Reminder emails per year (A3 × A5) | 48,000 | Calculated |
| A8 | Emails needing correction or follow-up | 3% | Illustrative assumption |
| A9 | Rework time per error | 20 min | Illustrative assumption |
| A10 | Loaded hourly cost – AR Clerk / AM | S$35 / S$60 | Illustrative assumption – replace with HR data |
| Activity | Calculation | Hours / year | Cost / year (S$) |
|---|---|---|---|
| AR list preparation | 4,800 × 10 min ÷ 60 | 800 | 28,000 |
| AM email writing | 48,000 × 6.5 min ÷ 60 | 5,200 | 312,000 |
| Error rework (AM) | 48,000 × 3% × 20 min ÷ 60 | 480 | 28,800 |
| Total | 6,480 | 368,800 |
Note that 80% of the cost sits with AMs, not with AR. That finding shapes the solution: automating the list alone would save little; drafting the emails is where the value is.
3. Proposed RPA Solution
An unattended bot runs on a schedule (for example, the first working day after month end and mid-month), replacing the ad hoc email trigger. Humans keep the two steps that need judgement: resolving exceptions and deciding whether a reminder goes out. This human-in-the-loop design reflects the review literature’s caution that RPA works best on structured, rule-based steps and needs governance where data quality or judgement is involved (Syed et al., 2020).
| To-Be step | Bot or human | Pain point addressed |
|---|---|---|
| Run and export Xero Aged Receivables at cut-off date | Bot | PP1, PP6 |
| Match each row to master list (exact name, then normalised name – case, punctuation, “Pte Ltd”) | Bot | PP3 |
| Filter past-due balance above 0; group by AM | Bot | PP4 |
| Route unmatched names, blank emails and duplicates to an exception queue with reason code | Bot to AR Clerk | PP3, PP5 |
| Resolve exceptions; update master list | Human (AR Clerk) | PP2, PP5 |
| Draft one reminder per customer with correct name, amount and email (Gmail drafts) | Bot | PP7 |
| Review, edit or hold drafts; click send | Human (AM) | Judgement retained |
| Write run log (rows processed, exceptions, drafts created) | Bot | PP5 – audit trail |
Out of scope for the pilot: CRM integration and attaching invoice breakdowns. Both are recommended for phase 2. Starting with one contained process and scaling once value is proven mirrors the path described in the Telefónica O2 case (Lacity & Willcocks, 2016).
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4. Cost-Benefit Analysis
4.1 Total Cost of Ownership – 3 years (illustrative)
All figures are illustrative planning estimates, not vendor quotes. A real submission should state its source for each line (for example a published vendor price list or an internal IT rate card).
| Cost category | Basis (assumption) | One-off (S$) | Annual (S$) |
|---|---|---|---|
| Software licensing | 1 unattended bot + orchestration, subscription | – | 20,000 |
| Infrastructure / cloud | Setup of virtual machine and credential store; hosting thereafter | 3,000 | 3,600 |
| Implementation and development | 40 days × S$800 (process design document, build) | 32,000 | – |
| Testing and deployment | 15 days × S$800 (UAT with sample AMs, hypercare) | 12,000 | – |
| Training and change management | 200 AMs × 0.5 h × S$60, plus AR training S$2,000 | 8,000 | – |
| Maintenance and support | Bot fixes when report layouts change, monitoring | – | 15,000 |
| Total | 55,000 | 38,600 |
3-year TCO = 55,000 + (3 × 38,600) = S$170,800
4.2 To-Be time and benefits (illustrative)
| Activity | To-Be assumption | Hours / year | Cost / year (S$) |
|---|---|---|---|
| AR exception handling | 4 min per request × 4,800 | 320 | 11,200 |
| AM draft review and send | 1.5 min per email × 48,000 | 1,200 | 72,000 |
| Residual rework | Errors fall by 80% | 96 | 5,760 |
| Total | 1,616 | 88,960 |
| Benefit | Value |
|---|---|
| Hours saved per year | 6,480 − 1,616 = 4,864 hours |
| FTE capacity released (1,800 productive hours per FTE – assumption) | ≈ 2.7 FTE |
| Processing time per request (AR + AM) | 75 min → 19 min (−75%) |
| Rework hours | 480 → 96 (−80%) |
| Gross labour value released | 368,800 − 88,960 = S$279,840 a year |
| Benefit realisation factor (assumption) | 50% |
| Annual benefit used in metrics | S$139,920 |
Why 50%? Released AM time is capacity that can go into collections calls and customer work, not an automatic cut in payroll. Research on RPA in public accounting firms reports staff being redeployed to higher-value work rather than released (Cooper et al., 2019). Counting only half the value keeps the case honest.
4.3 Non-financial benefits
- Control and auditability: every run leaves a log of rows, exceptions and drafts, which supports review in the way Moffitt et al. (2018) describe for audit automation.
- Consistency: the same matching rule and wording every cycle.
- Employee experience: AR staff move from copy-paste to resolving data issues; AMs spend time on conversations, not formatting.
- Customer experience: fewer reminders to the wrong contact or with the wrong amount, which protects relationships.
5. Key Financial Metrics
Time period: 3 years. Months 1–3 are build and test; benefits start in month 4, so year 1 receives 9 months of benefit. Cash flows are undiscounted; a fuller appraisal would add NPV at the organisation’s cost of capital.
| Item | Year 1 | Year 2 | Year 3 | Total |
|---|---|---|---|---|
| Benefits (S$) | 104,940 | 139,920 | 139,920 | 384,780 |
| Costs (S$) | 93,600 | 38,600 | 38,600 | 170,800 |
| Net (S$) | 11,340 | 101,320 | 101,320 | 213,980 |
5.1 Return on Investment
ROI = (Total benefits − Total costs) ÷ Total costs × 100
ROI = (384,780 − 170,800) ÷ 170,800 × 100 = 125.3% over 3 years
5.2 Payback period
Investment at go-live = one-off costs + 3 months of running costs = 55,000 + (3 × 3,216.67) = S$64,650
Monthly net benefit after go-live = (139,920 − 38,600) ÷ 12 = S$8,443.33
Payback = 64,650 ÷ 8,443.33 = 7.7 months after go-live = about 10.7 months from project start
5.3 Sensitivity
| Benefit realisation | 3-year ROI | Payback from go-live |
|---|---|---|
| 50% (base case) | 125.3% | 7.7 months |
| 25% (pessimistic) | 12.6% | 24.7 months |
| 22.2% | 0% (break-even) | – |
The pilot breaks even over three years as long as just over a fifth of the released labour value is realised.
6. Summary and Recommendation
The current reminder process is manual, error-prone and expensive mainly because of AM email writing, not AR list building. The proposed bot removes the list-building and drafting steps while leaving exceptions and the send decision with people. On stated illustrative assumptions it releases about 4,864 hours (2.7 FTE) a year, cuts rework by 80% and delivers a 3-year ROI of 125% with payback in about 11 months.
Recommendation: proceed with the pilot, subject to two conditions – confirm the past-due proportion and hourly rates from real data in month 1, and assign an owner for master-list data quality, since name matching without a customer ID remains the main delivery risk.
Need guidance on your own business case? Use this extract for reference only, then build your own figures. Get expert help or browse our SMU assignment help and accounting assignment help pages.
About the expert
William Tan holds a Master’s degree in Accountancy from the National University of Singapore and works full-time as an accountant with one of the biggest accounting firms in Singapore, according to his profile. His profile lists financial accounting, cost accounting, auditing and taxation among the topics he has written on, and 8 years of academic writing.
References
Cooper, L. A., Holderness, D. K., Sorensen, T. L., & Wood, D. A. (2019). Robotic process automation in public accounting. Accounting Horizons, 33(4), 15–35. https://doi.org/10.2308/acch-52466
Kokina, J., & Blanchette, S. (2019). Early evidence of digital labor in accounting: Innovation with robotic process automation. International Journal of Accounting Information Systems, 35, Article 100431. https://doi.org/10.1016/j.accinf.2019.100431
Lacity, M. C., & Willcocks, L. P. (2016). Robotic process automation at Telefónica O2. MIS Quarterly Executive, 15(1), Article 4. https://aisel.aisnet.org/misqe/vol15/iss1/4/
Moffitt, K. C., Rozario, A. M., & Vasarhelyi, M. A. (2018). Robotic process automation for auditing. Journal of Emerging Technologies in Accounting, 15(1), 1–10. https://doi.org/10.2308/jeta-10589
Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S. J. J., Ouyang, C., ter Hofstede, A. H. M., van de Weerd, I., Wynn, M. T., & Reijers, H. A. (2020). Robotic process automation: Contemporary themes and challenges. Computers in Industry, 115, Article 103162. https://doi.org/10.1016/j.compind.2019.103162
van der Aalst, W. M. P., Bichler, M., & Heinzl, A. (2018). Robotic process automation. Business & Information Systems Engineering, 60(4), 269–272. https://doi.org/10.1007/s12599-018-0542-4
