The Analyst's Secret Weapon: How We Can Save a 13K Dollar Leak in Our Supply Chain In 17 Minutes

As a business intelligence analyst, I love data. But let’s be honest: my real job used to be waiting. Waiting for data to be clean, waiting for a dashboard to render, and waiting for the right SQL query to return a useful answer.

In the past, this was a two-week project. Today, using Wren AI, it was a 17-minute conversation.

Here is the story of how Wren AI turned my passive reporting job into a proactive investigation, using the very questions and metrics we all struggle with every day.

Act I: The Red Herring and the Real Killer

I began with a core business question: Quality. My gut told me our defect rates were too high, eating into our margins.

The Wren AI Way: The Power of Iterative Questioning

Wren AI allowed me to bypass the vague answer immediately. I used an iterative approach, connecting simple facts to reveal a complex truth:

1. Identify the high revenue product type:

"Which product type generate the most revenue?"

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2. Data Integrity Check: (Crucial for ensuring my data was trustworthy)

"What is the average defect rate for items marked 'Fail' in their inspection results versus items marked 'Pass'?"

"Show me the average Defect rates by Product type."

3. The Drill-Down:

"Show me the defect rates by Supplier name and Product type."

The Insight:

The problem wasn’t “Skincare,” which only had a 2.9% defect rate. The real killer was Supplier 3.

• Cosmetics: 3.87% defect rate
• Skincare: 4.85% defect rate

In seconds, I went from blaming a product category to pinpointing a failing supplier that was quietly damaging our two biggest product lines.

Act II: The Profit Contribution Trap

Next, I focused on unit economics. We needed to find out which products were truly building wealth. I focused on the clearest metric: Net Contribution Per Unit (NCPU).

We all know that Skincare was our champion because it brought in the most revenue. However, this is the Volume Trap.

The Wren AI Way: Finding the Contribution Gap

I asked Wren AI to calculate the aggregate NCPU for all products sold.

"Calculate the average Net Contribution Per Unit for each Product type and show me its total sales volume."

The Insight:

We were losing money on every lipstick and powder sold. Meanwhile, the sales team was rewarded for pushing high-volume Cosmetics, even though its NCPU was negative. Haircare, the category we ignored, was quietly generating real profit.

In one sentence, Wren AI calculated a weighted, aggregated NCPU model that normally takes hours. It revealed that our strategy was misaligned with actual profitability.

Act III: The $13,166 Route Switch

If Haircare was our only profitable line, how could we maximize it? The answer was hidden in logistics.

The Wren AI Way: The Granular "What If"

I asked Wren AI a specific, comparative question:

"Compare the shipping costs of all Carrier on all Route for Haircare."

The Numbers Were Shocking

• Route A: $4.83 per unit
• Route B: $2.13 per unit
• Route C: $8.05 per unit

For the same carrier, Route B was almost 4× cheaper than Route C and we were shipping 2,224 units via the expensive route.

The Impact:

Switching from Route C to Route B saves $5.92 per unit:

This wasn't a theoretical projection. This was nearly $13,000 in immediate, hard cash savings found with a single question.

Conclusion: From Data Chaos to Cashflow Strategy

In one morning, Wren AI replaced weeks of manual effort with actionable, financially backed decisions:

1. Fix Quality: Put Supplier 3 on immediate notice for the 4.85% defect rate in Skincare.
2. Stop the Bleeding: Pause aggressive Cosmetics volume targets until manufacturing costs can be reduced by 75%.
3. Capture Instant ROI: Switch all Haircare shipments from Route C to Route B, unlocking $13,000+ in immediate savings.

Wren AI isn’t just an analysis tool, it’s a strategic catalyst. It helped me challenge the flawed “Cosmetics is King” narrative and walk into the boardroom with a plan to turn a $58 loss into a profitable future.

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