Case Studies
Business problems. Practical solutions.
Explore how I’ve used data and business analysis to investigate problems, improve processes and support better decisions. Interact with the dashboards and process maps to explore the work.
Interactive visuals use representative sample data to protect confidential information.
Case 01Finance · Procurement
Investigating missed supplier credits
Differences between supplier invoices and agreed contract terms meant money owed to the business was being missed. I investigated those discrepancies and helped introduce a more consistent validation process.
Tools: SQL · Excel · Stakeholder workshops
What I did
- 01Used SQL to reconcile supplier invoices against contracts, rebate percentages, buy prices and costs.
- 02Investigated discrepancies with Finance, Vendor Management and Customer Success.
- 03Supported the resolution of outstanding amounts and cash collection.
- 04Introduced invoice validation checks and an audit trail for follow-up.
Result
Supported the recovery of approximately $3 million in supplier credits within one year.
Supplier invoice reconciliation
Illustrative reconstruction using representative sample data.
Invoiced (sample)
$425,566
Per contract
$413,702
Total variance
$11,864
Invoiced above contract terms
Outstanding
$3,012
Flagged + under review
Recovered (sample)
$8,852
16 exception invoices
Invoiced vs contract value by supplier (sample, USD)
- Discrepancy
- Invoiced
- Per contract
Exception invoices — invoices above the contracted terms, with their current status.
| Invoice | Supplier | Month | Invoiced | Per contract | Variance | Type | Status |
|---|---|---|---|---|---|---|---|
| INV-2402 | Northline Supplies | 2024-08 | $19,123.50 | $18,300.00 | +$823.50 | Rebate not applied | Recovered |
| INV-2403 | Northline Supplies | 2024-09 | $4,389.00 | $4,200.00 | +$189.00 | Rebate not applied | Under review |
| INV-2405 | Juniper Packaging | 2024-11 | $27,192.00 | $26,400.00 | +$792.00 | Price mismatch | Recovered |
| INV-2406 | Juniper Packaging | 2024-12 | $4,389.00 | $4,200.00 | +$189.00 | Rebate not applied | Under review |
| INV-2408 | Juniper Packaging | 2025-02 | $8,151.00 | $7,800.00 | +$351.00 | Rebate not applied | Recovered |
| INV-2409 | Southport Timber | 2025-03 | $26,928.00 | $26,400.00 | +$528.00 | Quantity error | Under review |
| INV-2411 | Southport Timber | 2025-05 | $4,326.00 | $4,200.00 | +$126.00 | Price mismatch | Recovered |
| INV-2412 | Southport Timber | 2025-06 | $40,755.00 | $39,000.00 | +$1,755.00 | Rebate not applied | Recovered |
| INV-2414 | Cascade Ceramics | 2024-08 | $4,326.00 | $4,200.00 | +$126.00 | Price mismatch | Recovered |
| INV-2415 | Cascade Ceramics | 2024-09 | $4,326.00 | $4,200.00 | +$126.00 | Price mismatch | Recovered |
| INV-2417 | Elmfield Glass | 2024-11 | $27,588.00 | $26,400.00 | +$1,188.00 | Rebate not applied | Recovered |
| INV-2418 | Elmfield Glass | 2024-12 | $27,984.00 | $26,400.00 | +$1,584.00 | Missing credit | Recovered |
| INV-2420 | Elmfield Glass | 2025-02 | $8,151.00 | $7,800.00 | +$351.00 | Rebate not applied | Flagged |
| INV-2421 | Harbor Freight Co | 2025-03 | $27,192.00 | $26,400.00 | +$792.00 | Price mismatch | Recovered |
| INV-2423 | Harbor Freight Co | 2025-05 | $40,755.00 | $39,000.00 | +$1,755.00 | Rebate not applied | Flagged |
| INV-2424 | Harbor Freight Co | 2025-06 | $27,588.00 | $26,400.00 | +$1,188.00 | Rebate not applied | Recovered |
Case 02Retail · E-commerce
Understanding why customers returned products
Returns were rising, but the reasons were spread across order, product and returns data that nobody looked at together. The teams who could act on it had no clear view of which products, categories or reasons were driving the problem.
Tools: Power BI · SQL · Excel · Data modelling
What I did
- 01Cleaned and joined returns data with order, product and reason data.
- 02Analysed returns by product, category, reason and channel to find the real drivers.
- 03Built Power BI reporting tracking return rates and reasons over time.
- 04Shared findings with the teams responsible so actions targeted the biggest causes.
Result
My Power BI reporting helped reduce customer returns by 75%.
Customer returns analysis
Illustrative reconstruction using representative sample data.
Units delivered
153,565
Sample units
Units returned
8,601
Return rate
5.60%
Returned ÷ delivered
Top reason (problem area)
Wrong size
34% of returns
Return rate trend by month
Returned units by reason
Return rate by category (%)
Units delivered and returned, filtered by period, category and reason.
| Month | Category | Reason | Returned units | Delivered units | Share of month-category returns |
|---|---|---|---|---|---|
| 2024-07 | Home & Living | Wrong size ordered | 68 | 3252 | 2.1% |
| 2024-07 | Home & Living | Changed mind | 44 | 3252 | 1.4% |
| 2024-07 | Home & Living | Faulty on arrival | 36 | 3252 | 1.1% |
| 2024-07 | Home & Living | Not as described | 28 | 3252 | 0.9% |
| 2024-07 | Home & Living | Late delivery | 24 | 3252 | 0.7% |
| 2024-07 | Electronics | Wrong size ordered | 111 | 3692 | 3.0% |
| 2024-07 | Electronics | Changed mind | 72 | 3692 | 2.0% |
| 2024-07 | Electronics | Faulty on arrival | 59 | 3692 | 1.6% |
| 2024-07 | Electronics | Not as described | 45 | 3692 | 1.2% |
| 2024-07 | Electronics | Late delivery | 39 | 3692 | 1.1% |
| 2024-07 | Apparel | Wrong size ordered | 180 | 4507 | 4.0% |
| 2024-07 | Apparel | Changed mind | 116 | 4507 | 2.6% |
| 2024-07 | Apparel | Faulty on arrival | 95 | 4507 | 2.1% |
| 2024-07 | Apparel | Not as described | 74 | 4507 | 1.6% |
| 2024-07 | Apparel | Late delivery | 63 | 4507 | 1.4% |
| 2024-07 | Beauty | Wrong size ordered | 45 | 3250 | 1.4% |
| 2024-07 | Beauty | Changed mind | 29 | 3250 | 0.9% |
| 2024-07 | Beauty | Faulty on arrival | 23 | 3250 | 0.7% |
| 2024-07 | Beauty | Not as described | 18 | 3250 | 0.6% |
| 2024-07 | Beauty | Late delivery | 15 | 3250 | 0.5% |
| 2024-08 | Home & Living | Wrong size ordered | 69 | 3480 | 2.0% |
| 2024-08 | Home & Living | Changed mind | 44 | 3480 | 1.3% |
| 2024-08 | Home & Living | Faulty on arrival | 36 | 3480 | 1.0% |
| 2024-08 | Home & Living | Not as described | 28 | 3480 | 0.8% |
| 2024-08 | Home & Living | Late delivery | 24 | 3480 | 0.7% |
| 2024-08 | Electronics | Wrong size ordered | 108 | 3805 | 2.8% |
| 2024-08 | Electronics | Changed mind | 70 | 3805 | 1.8% |
| 2024-08 | Electronics | Faulty on arrival | 57 | 3805 | 1.5% |
| 2024-08 | Electronics | Not as described | 44 | 3805 | 1.2% |
| 2024-08 | Electronics | Late delivery | 38 | 3805 | 1.0% |
| 2024-08 | Apparel | Wrong size ordered | 127 | 3377 | 3.8% |
| 2024-08 | Apparel | Changed mind | 82 | 3377 | 2.4% |
| 2024-08 | Apparel | Faulty on arrival | 67 | 3377 | 2.0% |
| 2024-08 | Apparel | Not as described | 52 | 3377 | 1.5% |
| 2024-08 | Apparel | Late delivery | 45 | 3377 | 1.3% |
| 2024-08 | Beauty | Wrong size ordered | 28 | 2156 | 1.3% |
| 2024-08 | Beauty | Changed mind | 18 | 2156 | 0.8% |
| 2024-08 | Beauty | Faulty on arrival | 14 | 2156 | 0.6% |
| 2024-08 | Beauty | Not as described | 11 | 2156 | 0.5% |
| 2024-08 | Beauty | Late delivery | 9 | 2156 | 0.4% |
| 2024-09 | Home & Living | Wrong size ordered | 65 | 3496 | 1.9% |
| 2024-09 | Home & Living | Changed mind | 42 | 3496 | 1.2% |
| 2024-09 | Home & Living | Faulty on arrival | 34 | 3496 | 1.0% |
| 2024-09 | Home & Living | Not as described | 26 | 3496 | 0.7% |
| 2024-09 | Home & Living | Late delivery | 23 | 3496 | 0.7% |
| 2024-09 | Electronics | Wrong size ordered | 89 | 3348 | 2.7% |
| 2024-09 | Electronics | Changed mind | 58 | 3348 | 1.7% |
| 2024-09 | Electronics | Faulty on arrival | 47 | 3348 | 1.4% |
| 2024-09 | Electronics | Not as described | 36 | 3348 | 1.1% |
| 2024-09 | Electronics | Late delivery | 31 | 3348 | 0.9% |
| 2024-09 | Apparel | Wrong size ordered | 184 | 5179 | 3.6% |
| 2024-09 | Apparel | Changed mind | 119 | 5179 | 2.3% |
| 2024-09 | Apparel | Faulty on arrival | 97 | 5179 | 1.9% |
| 2024-09 | Apparel | Not as described | 75 | 5179 | 1.4% |
| 2024-09 | Apparel | Late delivery | 65 | 5179 | 1.3% |
| 2024-09 | Beauty | Wrong size ordered | 48 | 3914 | 1.2% |
| 2024-09 | Beauty | Changed mind | 31 | 3914 | 0.8% |
| 2024-09 | Beauty | Faulty on arrival | 25 | 3914 | 0.6% |
| 2024-09 | Beauty | Not as described | 19 | 3914 | 0.5% |
| 2024-09 | Beauty | Late delivery | 17 | 3914 | 0.4% |
Case 03Operations · Reporting
Replacing manual reporting with automated workflows
Recurring reports were rebuilt by hand every cycle — downloading exports, copying, cleaning and re-checking totals. The work was slow, easy to get wrong and depended on whoever remembered to do it.
Tools: PowerShell · Excel · AI-assisted workflows
What I did
- 01Mapped the manual steps in each reporting workflow and assessed what could safely be automated.
- 02Automated data collection, refresh and distribution using PowerShell.
- 03Used AI-assisted workflows to speed up building and validating the automation.
- 04Validated outputs against the manual versions before switching over, and documented the new process.
Result
Recurring manual reporting was replaced with validated, documented automated workflows. No time-saving figure is claimed.
Reporting automation workflow
Illustrative reconstruction using representative sample data. No time savings are claimed — only the workflow is shown.
Previous manual process (replaced)
Inputs
Processing
Validation
Outputs
PowerShell: collect & merge
A scheduled PowerShell script collects the source files, merges them and standardises columns and formats. Replaces manual download, copy-paste and cleaning steps.
Click any step to see what it does, which manual steps it replaces and which controls are kept. In my experience this kind of automation removes repetitive manual steps, reduces copy-paste errors and frees time for the analysis itself.
Case 04Supply Chain · Sales
Making supplier delivery problems visible
Late deliveries were affecting sales, but purchase, delivery and sales data sat in separate places. Without a shared view, supplier follow-up relied on anecdote rather than evidence.
Tools: SQL · Power BI · Excel
What I did
- 01Consolidated purchase, delivery and sales data into one model.
- 02Measured delivery performance and late orders by supplier, segment and period.
- 03Connected supplier delivery issues to the sales they affected.
- 04Delivered reporting that gave teams a shared evidence base for supplier follow-up.
Result
Identified delivery issues and improved visibility for supplier follow-up.
Supply chain & sales performance
Illustrative reconstruction using representative sample data.
Orders
12,805
Sample orders
Late deliveries
1,295
On-time delivery
89.9%
Below 95% target
Sales
€6,602,387
Sample revenue
Margin
27.3%
Revenue-weighted
Sales and margin trend by month
- Margin % (dashed)
- Sales (€)
Late orders by supplier
Supplier detail (filtered)
| Supplier | Orders | Late | On-time | Sales | Margin |
|---|---|---|---|---|---|
| Northline | 2245 | 405 | 82% | €1,178,659 | 27.6% |
| Juniper | 2254 | 335 | 85.1% | €1,197,472 | 27.1% |
| Southport | 2199 | 185 | 91.6% | €1,137,935 | 27.9% |
| Cascade | 2088 | 159 | 92.4% | €1,059,454 | 27.1% |
| Elmfield | 2099 | 132 | 93.7% | €1,038,772 | 26.5% |
| Meridian | 1920 | 79 | 95.9% | €990,095 | 27.4% |
Delivery performance against sales and margin, filtered by period, supplier and segment.
Case 05Finance · Process Improvement
Designing the invoice validation process and audit trail
Finding discrepancies was only half the problem — without a defined process, flagged invoices could still be paid and outstanding amounts forgotten. The business needed clear checks, owners and follow-up.
Tools: Process mapping · Requirements gathering · Stakeholder discussions · SQL · Excel
What I did
- 01Agreed the validation checks with Finance, Vendor Management and Customer Success.
- 02Defined responsibilities: who validates at intake, who reviews exceptions, who approves recoveries.
- 03Designed exception handling so flagged invoices enter a queue with a status and an owner.
- 04Implemented the audit trail recording each check, finding, status and outstanding amount.
Result
Invoice validation checks and an audit trail were implemented, identifying discrepancies and tracking outstanding amounts to resolution.
Invoice validation & audit trail
Illustrative reconstruction based on implemented work. AS-IS shows the process before the change; TO-BE shows what was introduced.
Click any step in the map to see what happens, who is responsible and where the bottlenecks and exception paths are.
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