Selected Work
A cross-section of the platforms and monitoring tools designed and delivered alongside core finance leadership responsibilities, plus a few smaller analytical projects.
Challenge: Senior management needed faster, more consistent visibility than periodic spreadsheet reporting allowed.
Solution: A finance-led reporting platform with live dashboards, built to progressively expand across operational modules.
Read the case studyChallenge: No systematic way to monitor fuel consumption or flag abnormal usage across a large mixed fleet.
Solution: Asset-specific benchmarking, operator-level monitoring and exception reporting for a fleet of 100+ vehicles.
Read the case studyChallenge: Document tracking and payment readiness across suppliers relied heavily on manual checks.
Solution: A workflow-controlled system strengthening duplicate-payment prevention and document visibility.
Read the case studyChallenge: Export proceeds needed structured tracking from inflow through invoice/LOC matching to closure.
Solution: An operational prototype with bank-mismatch flagging and a 60-day follow-up escalation view.
Read the case studyA standalone Python-based executable that consolidates departmental budget workbooks into a controlled master template, with an audit-output workbook and a simple interface for non-technical staff.
Built to remove the manual, error-prone reconciliation of separate department budget files into one controlled workbook, while preserving a clear audit output of how the master template was assembled.
An analytics study examining task-achievement rates across a large historical workforce dataset, with scope to explore seasonality, field-condition and crop-age effects.
Part of the broader workforce analytics work covering 1,560+ employees, aimed at giving operational leadership a quantitative view of task achievability rather than relying solely on anecdotal reporting.
A block-level machine learning study using historical production and climatic variables — rainfall, rain days, temperature, humidity and solar radiation — to evaluate yield-prediction performance.
Completed as part of MSc Business Analytics coursework, applying applied machine learning methods to an agricultural production dataset.
Get In Touch
Available for remote consulting, fixed-scope diagnostics, and finance-systems project work.
