All work

Data platform

Analytics platform

One analytics platform across procurement, inventory, and on-water sales: shared filters, governed measures, and a drill path from headline number to individual line.

Role
Data architecture, analytics design, full-stack build
Timeline
TBD
Status
In production
Client
Food Processor & Importer — commercial and supply-chain analytics
Analytics platform interface
Client work shown anonymized — walkthrough on request

The problem

Purchasing, inventory, and sales each analyzed the business in separate spreadsheets. Nobody could follow a purchase order through to yield and margin, see what aged inventory was actually costing, or quote product already on the water without rebuilding a workbook by hand.

The approach

A single semantic layer defines the measures once — pounds, yield versus target, recovery, value, age band, margin — and every page reads them. Each view opens with an executive strip, then a filter rail and interactive visuals, then the underlying ledger, so a headline number can be drilled to the lot or line that produced it. Themes ship light and dark; every table exports.

The result

Procurement, operations, and sales work from the same measures, and questions that used to take a spreadsheet build now resolve in a few clicks. Cycle-time figures: TBD.

The stack

LovableTypeScriptPostgresSupabaseSemantic metrics layerrechartsExcel / PDF export

Inside the app

Real screens with branding blurred, or reproduced with anonymized data — no client names, logos, or user identities.

Inventory aging: value by age band, warehouse profile and staleness, treemap and warehouse × age-band matrix, down to the inventory-line ledger.
Purchase-order lineage: received pounds converted into runs, primal yield against target, recovery and margin, with a per-PO panel showing cost in and value out. Supplier names blurred.
On-water inventory: arrival timeline by ETA week, volume by country and sub-primal family, and quotable lines with data-quality warnings surfaced up front.

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