LinkedIn / case study

A weekly commercial pack built around the decision.

An anonymised weekly workflow for forecasting, replenishment and allocation decisions.

Illustrative context / original source data and company details are not shown.

Outcome~4 h baseline · ~2 min run · ~10 min review

Evidence levelMeasuredBefore/after comparison documented; review remains human.

StackPython · pandas · forecasting · QA

01 / Context

The question behind the work.

Commercial inputs arrived in separate files and required repeated preparation before a weekly decision conversation. The useful product was not another dashboard; it was a reviewable pack with the right context.

02 / Built

Structure before decoration.

Joined recurring inputs, standardised the preparation steps and produced a decision-ready output with a human review gate. The workflow keeps assumptions visible instead of hiding them behind an unexplained automation.

03 / Result

What changed.

The documented comparison is approximately four hours of manual preparation versus two minutes of run time plus ten minutes of review. This is a per-run comparison, not an aggregate annual saving claim.

This case study is intentionally anonymised. It describes the shape, scale and outcome of the work without exposing internal data, client systems or confidential screenshots.

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