How to Use Supply-Chain Data for Customer Read-Through

Key findings

• Supplier revenue percentage can rank which suppliers have the most revenue associated with a customer before those suppliers report.
• SK Hynix to Nvidia at 27.88%, LG Energy Solution to Tesla at 19.03%, and Micron to Nvidia at 17.62% illustrate materially different customer concentration levels.
• Relationship size provides an absolute scale check, while customer cost percentage answers a separate customer-side question.

To identify the suppliers most exposed to a customer's demand change, rank the customer's mapped suppliers by supplier revenue percentage, then verify that the event affects the product or business behind each relationship. This is customer read-through: using a customer's earnings, guidance, demand change, or production update to decide which suppliers deserve attention next.

The key supply-chain metric is supplier revenue percentage. It estimates how much of a supplier's revenue is associated with one customer relationship. The higher the percentage, the stronger the reason to investigate whether a material customer event could matter to the supplier. Real Altsets relationships show how quickly the ranking can change:

If the starting event is at Nvidia, the displayed data makes SK Hynix and Micron obvious read-through candidates. If the event is at Tesla, LG Energy Solution belongs near the top of the supplier research queue. The method is the same even though the companies and industries differ.

What customer read-through is trying to answer

The question is not simply, "Who supplies this company?" The useful question is: Which suppliers have enough revenue tied to this customer that a major customer event could be economically relevant to them?

A supplier directory cannot answer that by itself. Ten suppliers can all be connected to the same customer while having very different levels of revenue concentration. Supplier revenue percentage turns the relationship graph into a prioritization tool.

Step 1: start with a real customer event

Customer read-through only becomes useful when there is a reason to investigate. Examples include earnings guidance, unit shipment changes, capital expenditure changes, production cuts or ramps, inventory corrections, product delays, major customer wins or losses, and geographic demand changes.

The event creates the research direction. Supply-chain data tells you where to look next.

If Nvidia materially changes AI infrastructure demand, that event should not be pushed equally through every company connected to Nvidia. A supplier associated with 27.88% of its revenue from Nvidia deserves a different level of attention than one associated with a very small revenue share.

Step 2: rank suppliers by revenue concentration

Consider the two Nvidia examples. Altsets estimates SK Hynix to Nvidia at 27.88% of SK Hynix revenue and Micron Technology to Nvidia at 17.62% of Micron revenue.

Those values do not forecast what either supplier will report. They tell you that Nvidia represents a meaningful part of the mapped revenue base for both suppliers, with the displayed concentration higher for SK Hynix. A basic read-through workflow would therefore begin by asking:

1. What exactly changed at Nvidia?
2. Which mapped suppliers have the largest supplier revenue percentages?
3. Is the customer event relevant to the product or service likely represented by those relationships?
4. Has the supplier already reflected the change in its own guidance or valuation?
5. What additional evidence is needed before making a financial conclusion?

The relationship data narrows the research universe. It does not replace company-specific analysis.

Step 3: separate demand relevance from supplier criticality

Supplier revenue percentage answers a supplier-side question. Customer cost percentage answers a customer-side question. Those two perspectives should not be mixed.

The LG Energy Solution to Tesla relationship is a useful example. Tesla is associated with an estimated 19.03% of LG Energy Solution revenue, while LG Energy Solution is associated with 3.41% of Tesla COGS.

For customer read-through, the 19.03% figure is the important starting point. It says Tesla appears economically important to LG Energy Solution's revenue base.

The 3.41% customer cost estimate answers a different question about Tesla's exposure to the supplier. This distinction prevents a common mistake: using whichever percentage looks largest without asking which company's financial statement it is measured against.

Step 4: use relationship size as a scale check

Revenue concentration can look large because the supplier is small. Relationship size adds absolute context.

The Micron to Nvidia relationship is estimated at 9.8B USD and 17.62% of Micron revenue. Both measures point to a meaningful relationship.

Lam Research to Micron is estimated at 1.2B USD and 5.61% of Lam Research revenue. That is still economically visible, but it belongs in a different priority tier. The combination of percentage and absolute size is more informative than either measure alone.

What this method can reveal before supplier earnings

The value of customer read-through is timing.

A major customer's earnings call or production update may happen before a supplier reports. If the supplier has meaningful mapped revenue exposure to that customer, the customer event becomes a research input for the supplier before the supplier's own results arrive.

That does not mean the supplier's next quarter can be calculated directly from the relationship percentage. The percentage does not reveal contract timing, inventory buffers, pricing changes, product mix, offsetting customers, shipment timing, currency effects, and whether the customer event affects the exact product represented by the relationship. The method is best used to find where expectations may need to be revisited, not to mechanically translate one company's guidance into another company's revenue.

When customer read-through is most useful

This use case becomes especially useful when: one customer represents a large share of supplier revenue, the customer provides frequent operational updates, the supplier reports later than the customer, the relationship is tied to a cyclical or fast-moving end market, consensus expectations appear slow to react, and several suppliers can be ranked against the same customer event. The method becomes weaker when relationship coverage is incomplete, when the product mapping is unclear, or when the customer's update has little connection to the supplier's likely product category.

A repeatable research workflow

A practical workflow is:

1. Identify a material customer event.
2. Map first-degree suppliers to that customer.
3. Rank suppliers by supplier revenue percentage.
4. Use relationship size to separate high percentages attached to small relationships from genuinely large exposures.
5. Check product and business context.
6. Compare the event with supplier guidance and market expectations.
7. Keep missing values as unknown.
8. Record what is observed, what is inferred, and what remains a hypothesis.

The Micron and Nvidia analysis shows one company-specific version of this method. The Tesla and LG Energy Solution analysis shows why the supplier-side percentage can differ sharply from the customer's cost exposure.

For metric definitions and limitations, read the Altsets supply-chain data methodology. Browse Supply-Chain Data Use Cases for other methods built from the same relationship graph.

Methodology

Read the methodology for this research.

Related research

• Micron and Nvidia: How Much Revenue Exposure Sits in One AI Memory Relationship?

• How to Trace Earnings Surprises Through a Supply-Chain Network

• SK Hynix, Nvidia, and Apple: Which Relationship Is More Economically Important?

• LG Energy Solution and Tesla: Which Side Has More Relationship Concentration?