Client Stories
Case Study 02Anonymised

Turning import orders data into clearer buy, import, and caution signals.

Project Ardent shows how a transparent data model can support a recurring commercial decision without hiding the assumptions operators need to review.

Clear decisions backed by data.

Chapter 1

Where they were

At Ardent, a core commercial challenge was deciding whether to import a SKU or buy locally, often months before goods would arrive.

Early imports could benefit from tighter markets, but if conditions softened the business risked holding high cost inventory in a weaker pricing environment.

Ardent had detailed monthly import orders data covering all importers by SKU, which gave visibility into future supply. The dataset was difficult to operationalise, so decisions were largely driven by experience rather than a structured view of how demand, local production, and imports would interact.

Chapter 2

What we did

The work turned raw import orders into a recurring decision model: simple enough to review, structured enough to compare supply, demand, timing, and downside risk.

1

Built a practical import decision model

We cleaned and consolidated monthly import orders data, then aligned each order with its expected arrival window at port. Raw filings became an estimate of imported supply by future period.

2

Built the demand-supply framework

We forecast country demand for each SKU and estimated local production where possible. Comparing net demand with expected imported volumes highlighted when markets were likely to tighten or move into oversupply.

3

Turned data into signals and risk

For each cycle, Ardent received a structured view of where demand and supply were expected to sit for each key SKU, whether conditions favoured importing, buying locally, or taking a cautious stance, and the downside risk of each decision.

4

Iterated with monthly feedback

The Ardent team shared fresh import orders data each month. We refreshed the analysis, reviewed how previous signals played out, and improved the model while keeping it transparent.

Chapter 3

Where they are now

The engagement gave Ardent a clearer data-driven foundation for import decisions that were previously driven by intuition.

The business is now better able to time imports into tighter markets, avoid oversupply risk, and protect margins while treating imports as a managed commercial lever aligned with market conditions.

Related paths
What the retainer produces

A recurring commercial call became a structured monthly decision workflow.

This is the retainer pattern in a data-heavy workflow: structure the source data, expose the assumptions, ship the signal, and improve the model as the team sees how it performs.

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