WTF does a Supply Chain Analyst actually do?

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You saw a job posting. "Supply Chain Analyst — 5-10 LPA (Indian Currency) — SQL, Excel, Power BI."

You applied. You got a call. And then they asked: "So, what do you know about supply chain analytics?"

You froze.

Because you’ve spent 3 months learning RANK() and DENSE_RANK(), but you have absolutely zero idea what a Supply Chain Analyst actually does on a Tuesday morning.

This is the gap. It’s not SQL. It’s not Python. It’s Domain Knowledge.

Let me walk you through the job like you’re joining the team on Monday.

First, what is "Supply Chain" in simple terms?

A company makes or buys a product. That product needs to travel from a factory (or a warehouse) to the customer's hands.

Everything that happens in between, purchasing raw materials, manufacturing, storing in warehouses, shipping, delivering, that’s the Supply Chain.

Your job as a Supply Chain Analyst? Make sure this chain doesn't break. And when it does break, figure out why and how much money it cost us.

That's it. Everything else is just details.

Your Actual Monday Morning

You open your laptop. You have 3 tabs open: Outlook, a Power BI dashboard, and SQL Server. Your manager pings you:

❝

"The Delhi warehouse had 340 orders yesterday but only shipped 285. What happened?"

This is the job. Not building ML models. Not creating fancy visualizations. Figuring out why 55 orders didn't ship.

Here is exactly how you’d think through it:

Step 1: Check the Data Integrity

Pull yesterday's orders for the Delhi warehouse. Were all 340 orders actually confirmed? Or were some cancelled by the user before shipping?

SQL

SELECT 
    order_status, 
    COUNT(*) as order_count
FROM orders
WHERE warehouse = 'Delhi' 
  AND order_date = '2026-02-13'
GROUP BY order_status;

The Find: You see 320 confirmed, 15 cancelled, and 5 payment_failed. The Insight: The real gap isn't 55 orders. It’s 320 (valid) - 285 (shipped) = 35 unshipped orders.

Step 2: Find the Bottleneck

Of those 35 unshipped orders, were the products even in stock? Or did the warehouse run out?

SELECT 
    p.product_name, 
    o.quantity_ordered, 
    i.quantity_on_hand
FROM orders o
JOIN inventory i 
  ON o.product_id = i.product_id 
  AND i.warehouse = 'Delhi'
JOIN products p 
  ON o.product_id = p.product_id
WHERE o.warehouse = 'Delhi' 
  AND o.order_date = '2026-02-13'
  AND o.order_status = 'Confirmed'
  AND o.shipped = 0;

The Find: For 30 of those orders, quantity_on_hand is 0. The Answer: Stockout. The warehouse physically didn't have the product to ship.

Step 3: Report It (The most important part)

You don't send your manager a SQL query. You send them this:

❝

"35 confirmed orders failed to ship yesterday.

Root Cause: 30 were stockouts: we ran out of SKU-4421 (Wireless Earbuds). Secondary Issue: The remaining 5 are stuck in address verification. Recommendation: We need to expedite the restock PO for the Earbuds immediately."

Notice what happened? You started with data. You ended with a business recommendation.

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The 5 Metrics You Will Live With

Every domain has its own vocabulary. If you walk into an interview knowing these 5 terms, you are already ahead of 90% of fresher applicants.

1. Fill Rate Of all the orders placed, what percentage did we actually fulfill?

Example: Customers ordered 1,000 units. We shipped 920. Fill Rate = 92%.
Reality: If this drops below 95%, the VP is screaming at someone.

2. Stockout Rate How often is a product unavailable when a customer wants it?

Why it matters: A 5% stockout rate means for every 100 customers, you told 5 of them "Sorry, go buy it from Amazon instead." This kills businesses.

3. Inventory Turnover How many times did you sell through your entire inventory in a year?

High Turnover (8.0+): Efficient. You sell stuff fast.
Low Turnover (<2.0): Bad. Money is sitting on shelves gathering dust.

4. Lead Time The time gap between placing an order with a supplier and receiving it in your warehouse.

The Trap: If your supplier takes 21 days to deliver, but you only have 14 days of stock left... you are already in trouble.

5. OTIF (On-Time In-Full) Did the customer get exactly what they ordered, when they were promised? This is the holy grail of customer satisfaction.

The Interview Question :

"Our inventory costs have increased 18% this quarter, but revenue is flat. What would you investigate?"

Bad Answer: "I would check the database."

Good Answer (The Analyst Mindset): "I would break this down into three hypotheses:

Dead Stock: Are we ordering too much of the wrong products that aren't selling? I'd check inventory_turnover by SKU.
Supplier Costs: Did our vendors raise prices? I'd compare unit_cost this quarter vs. last quarter.
Seasonality: Is this a strategic buildup? Sometimes costs go up before Diwali or Black Friday because we are stocking up on purpose."

You don't need to solve it instantly. You need to show you know where to look.

See you in 2 days.
—RAJ (Real Analyst Jobs)

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