Business Intelligence / Process Automation · Professional experience
Porta Hnos.
Turning fragmented budget and financial information into a consistent analytical flow for decision support.
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Context
The need originated with the company owners and was channeled through Administration Management.
The objective was to provide a weekly view of budget status by project and area: budgeted versus actual spending, planned investments and future commitments.
Information was distributed across the ERP, spreadsheets and informal operational knowledge.
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Problem
The challenge was not simply to combine files.
Accounting movements, budget information, future purchases and investments, and knowledge held by responsible teams had to be combined.
The problem was to turn fragmented data and knowledge into consistent decision-support information.
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Discovery and definition
I worked with Administration Management and budget and investment owners to understand what information leadership needed and where it could be obtained.
For each project I reconstructed not only what had been spent, but what investments were expected, why they were needed, when they might occur and their possible economic impact.
When information existed in the ERP I coordinated a SQL view with IT; information not formally recorded was gathered directly from its owners.
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Solution build
I designed a custom flow to integrate the different sources.
Python ingested and cleaned accounting information, processed movements and cash outflows, and combined them with budgets, investments and future needs.
- ERP and SQL view
- Excel and owner-maintained sources
- Discovery of future commitments
- Python for integration, cleaning and calculation
- Bot, executive reports and Power BI
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Engineering economics and analysis
Consolidated information was only the starting point.
I evaluated investments through their technical and economic logic using my Industrial Engineering background.
Depending on the case, I analyzed expected savings, financing cost, maintenance, debt cost and the relationship with gross margin.
When appropriate, I used NPV and IRR.
Analyses combined Argentine pesos and US dollars and incorporated bank interest rates when relevant.
Final investment decisions remained with management and the corresponding owners.
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Budgets, investments and purchasing
The solution allowed each budget, project and investment to be reviewed while incorporating upcoming purchases and future needs.
Part of the information was distributed through a bot: JavaScript handled interaction while Python performed calculations and processing.
The same pattern was later applied to Purchasing to analyze the financial cost of raw-material and supply acquisitions.
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Result
Information moved from fragmented systems, files and individual knowledge into a consistent analysis and reporting flow.
My responsibility was to build the information and analysis supporting decisions, not to replace the management-control function or management's final decision ownership.