Bloomberg’s Supply Chain Algorithm: Providing
Insight Into Company Relationships
I. Bloomberg Supply Chain Algorithm (BSCA)
In the SPLC diagram for Texas Instruments (TXN US Equity) below, we show WPG Holdings (3702 TT
Equity) as accounting for 5.85% of TXN’s revenues, and TXN accounting for 7.09% of WPG’s COGS.
Mousing over the relationship reveals a tooltip which shows the source to be “Bloomberg Estimate.”
How did we generate this data?
Bloomberg has an algorithm which creates quantified customer and supplier relationships where no
quantified relationship exists publicly. This white paper provides insight into how Bloomberg’s Supply
Chain Algorithm (BSCA) estimates quantified relationships.
BSCA relies on many types of data, including but not limited to financial, accounting, end market,
channel, and product-level data. Before an estimate of a relationship’s value can be made, however, we
must first know that an underlying relationship exists. (We call a relationship of unknown value an
“unquantified” relationship, and one with a known or estimated value a “quantified” relationship). This
underlying or foundational unquantified relationship can come from either the customer or the supplier
side of the relationship, or from other sources.
Once we have evidence of an unquantified relationship, how can we know the size of it? More
specifically, in the case of TXN and WPG mentioned above, how can we know how much of TXN’s
revenue is derived from WPG? Or how much of WPG’s costs that TXN is? We have to gather additional
information.
First, we must define how the product or service that is being sold by the supplier is accounted for at the
customer. Costs can fall into several accounting “buckets,” including cost of goods sold (COGS), capital
expenditures (CAPEX), research and development (R&D), and sales, general, and administrative (SG&A)1.
In the case of TXN-WPG, we know that WPG is a distributor that resells TXN’s semiconductors, so all of
TXN’s revenues from WPG would be accounted for by WPG as COGS.
Next, BSCA establishes a series of mathematical limits as it begins to define the size (or range of possible
sizes) of the relationship. Relationships cannot amount to just any percentage: we know that limits exist,
because the revenues TXN receives from WPG in any given period are finite – they must fall somewhere
between 0% and 100% of its total revenues. Similarly, we know that TXN must account for somewhere
between 0% and 100% of WPG’s COGS (since we have already established that line item as the
accounting type).
1 Additionally, a single relationship between two companies can be accounted for in more than one bucket. An
example would be a computer company such as Hewlett-Packard (HPQ US Equity) selling to a distributor such as
Ingram Micro (IM US Equity). The HPQ product that Ingram Micro resells would be accounted for as COGS,
whereas the HPQ product that IM keeps for internal use would be accounted for as CAPEX. Accounting
classification is not disclosed by companies, but is made by Bloomberg analysts. Further, any segmenting of
accounting types is also done by analysts.
Figure 1. Establishing Range Of Relationship Possibilities
Source: Bloomberg
Imagine a grid of possible values. If we plot the relationship in percentage terms as a function of TXN’s
revenues on the y-axis and WPG’s COGS on the x-axis, we then have a grid of 100*100=10,000 possible
values (absent additional data or analysis). But this simple calculation assumes that relationships happen
in whole-percent integers, which is unreasonable. BSCA calculates to the thousandth of a percent2; thus
our grid includes 10 billion3initial possible values for any one relationship (absent any additional data or
analysis). How do we get from 10,000,000,000 to just a single point? In theory, in the grid we have
constructed above, the odds of randomly selecting the precise value (again absent any additional data
or analysis) are very long at 10,000,000,000:1.
How can we get these odds more in our favor – and more importantly, have some assurance that our
estimates are reasonable? Using other data, we can greatly limit the range of possible values, and
additionally provide confidence intervals for our single-point estimate.
First, BSCA sets a lower limit of 0.001% for any relationship4 . Next, it establishes a ratio between the
supplier’s revenue and the customer’s costs, and the portion of each that is applicable to the other. This
2 More specifically, BSCA assumes that any foundational unquantified relationship between a supplier and its
customer is at least 0.001% of the supplier’s revenue.
3 100,000 * 100,000
4 For a company like TXN, which had $13,735,000,000 in 2011 revenues, this would equate to $137,350 on an
annual basis. While BSCA operates at the thousandth of a percent, it stops well short of working at the smallest
possible unit (which would be $0.01 in U.S. dollar terms, or the lowest possible denomination in other currencies).
The wide range of relationship values and sizes that BSCA utilizes necessitates operating at this level; within any
particular supply chain, larger relationships can be several orders of magnitude larger than smaller ones, and thus
4
ratio is a line, which greatly limits the range of possible values on the grid. In fact, assuming that costs
are accounted for correctly, all possible values for a relationship must fall on this line – they cannot exist
anywhere else within the grid. Using our 0.001% limit and increment, this financial and accountingbased ratio line would have a length ranging from 100,000 (a technically undefined zero degrees or 90
degrees on the x or y axes, respectively) to 141,421 5 Framing our problem in these terms, the possible
values in our grid shrink from 10,000,000,000 to 100,000. Even if the line itself is longer than 100,000
units (as it virtually always will be, because a perfectly horizontal or vertical line would be technically
undefined, and these two cases also represent the shortest possible example), from the suppliers
perspective we are analyzing its revenues; thus the primary value will be on those terms. From a
revenue (y-axis) perspective this line can have a range of possible values from 0.001 to 99.999. Thus we
have decreased the range of possible values from 10,000,000,000 to 99,999, which represents a decline
of over 99.999%, or a shift in odds from 10,000,000,000:1 to ~99,999:1, if we were to select a value on
this line randomly, based on this new information. While these odds are still very long, they are greatly
reduced from what they were when we started.
Let’s apply this methodology to our example with TXN and WPG. Based on financial data from both
companies for the quarter ending December 31, 2011, we can begin to limit the potential size of the
relationship. WPG’s COGS for Q4:11 (converted to US$) were ~$2.575B 6; TXN’s revenues for the same
period were ~$3.420B. Thus the ratio of WPG’s COGS to TXN’s revenues is 0.753:1. Put another way, this
means that if TXN were to account for 100% of WPG’s COGS – certainly not the case, since we know that
WPG distributes semiconductors from companies other than TXN – then it is impossible for WPG to be
more than ~75.3% of TXN’s revenues up to this point in our analysis, because at that level, TXN would
amount to over 100% of WPG’s COGS. This is a limit that cannot be exceeded, or even reached at all.
But this ratio is not quite right, because not all of TXN’s revenues are from the sale of semiconductors,
and hence not all can be applied to WPG. About 5% of TXN’s revenues come from calculators and
royalties; when we add that to the equation, the ratio of WPG’s COGS to TXN’s semiconductor revenues
is 0.793:1, which means that WPG cannot be more than 79.3% of TXN’s revenues – this is a necessary
step, but it doesn’t help us lower our limit.
We can go a step further on WPG’s side: like TXN, not all of WPG’s revenues are derived from the sale of
semiconductors. For the quarter ending December 31, 2011, WPG derived ~89.4% of its revenues from
the sale of semiconductors. Assuming a similar split with its COGS, we assume that TXN’s semiconductor
revenues would only apply to that portion of WPG’s COGS that is related to semiconductors; thus we
come up with a ratio of 0.709:1, meaning that, taking into account both the portion of TXN’s revenues
that are exposed to WPG and the portion of WPG’s costs that are exposed to TXN, WPG cannot exceed
working at a not-as-granular level of detail would effectively render many relationships as zero erroneously.
Conversely, operating at the smallest possible discrete level ($0.01) is unnecessarily detailed.
5
A line with a length of 100,000 is simply the number of units on the x or y-axis in 0.001 increments. The 141,421
2
2
2
length is derived via the Pythagorean theorem: 100,000 + 100,000 = 141,421 . This maximum-length line happens
only in the case where one company’s applicable revenue is exactly equal to the other company’s applicable costs,
whatever the accounting type may be.
6
On a consolidated basis.
5
~70.9% of TXN’s revenues for the quarter ending December 31, 2011, because any higher percentage
would exceed 100% of WPG’s COGS. Consistent with our view from a revenue perspective, this means
that we have 70,884 7 possible values on our ratio line. This is our first established maximum limit 8.
Figure 2. Estimating Range Of Relationship Possibilities (Step 1)
Source: Bloomber0067
While this is a significant step in the right direction – we have decreased the odds of randomly selecting
the correct value by nearly 30%, from 99,999:1 to 70,884:1 – we are still left far short. The value of the
TXN-WPG relationship (in percentage terms) is somewhere on the ratio line that we have created, and
absent additional data, an individual would have a ~0.0014% chance 9 of correctly guessing the point it
occupies on that line (absent additional data or analysis). A lot of work remains to be done.
BSCA examines still more data. TXN states in its public filings that distributors are ~37% of its revenues.
We know WPG is a distributor; thus, with this information we can set 37% as a new upward limit for
TXN’s reliance on WPG for revenues. If WPG were 37% of TXN’s revenues, based on our previously
7
We had rounded this to 0.709 in a previous example, as we were working in thousandth increments.
As BSCA works through company relationships globally, these upward limits can become very small – less than
one percent – in relationships where the supplier is much larger than the customer. Take for example the case of
Flextronics (FLEX US Equity), which provides electronic manufacturing services to F5 Networks (FFIV US Equity).
Based on correctly identifying the accounting (COGS) for the relationship and having the underlying financials for
both companies for the quarter ending December 31, 2011, it becomes impossible for FFIV to be more than
~0.74% of FLEX’s revenues, because at this level and above, the amount would exceed 100% of FFIV’s COGS.
9
1/70,884
8
6
established ratio, this would equate to ~52.2% of WPG’s COGS. Of course, logic dictates that WPG must
also be less than 37% of TXN’s revenues, because TXN has other distributor customers as well – thus
WPG would not be 100% of TXN’s distributor revenues. But for the time being, we have decreased the
odds of randomly selecting the percentage value of the TXN-WPG relationship by nearly 48%, from
1:70,844 to 1:37,000.
Figure 3. Estimating Range Of Relationship Possibilities (Step 2)
Source: Bloomberg
What else do we know? In the United States, the SEC dictates that public companies which trade on a
US-based stock exchange must disclose customers that comprise 10% or more of revenues on an annual
basis 10. TXN has not disclosed WPG as a customer in this way; thus, we know that WPG must be less
than 10% of revenues 11.
Further, we know of at least 15 distributor customers of TXN – none of which are greater than 10% of
TXN’s revenues (due to no disclosure from TXN). If we believed that these 15 distributors represented
10
Existing practice among publicly traded companies in the US reveals that there is varying interpretation of this
rule (SEC Reg. S-K 101.c.1.viii). Occasionally, companies don’t disclose the name(s) of any customer(s) over 10% of
revenues, instead referring to them as “Customer A,” “Customer B,” etc. Additionally, companies sometimes look
“through” distribution and/or EMS companies to the “end user” of its products. At some level, BSCA has to rely on
the classification that the companies themselves disclose.
11
So the question is begged: why do we bother going through the previous steps when the SEC-based disclosure
requirement more quickly “cuts to the chase” in this particular case? The answer is because most of the 28,000
public companies that we are currently covering from a supply-chain perspective do not trade on a US-based
exchange, and thus are not subject to the SEC reporting requirement.
100% of TXN’s distributor customers (the 37% limit), then given the data up to this point, the largest a
distributor customer of TXN could possibly be, in theory, is this disclosure limit. But this logic wouldn’t
apply to all of the 15 distributor customers – only those with attributable COGS large enough to
sufficiently account for 10% or more of TXN’s revenues12. As we’ve discussed, WPG meets this criteria. If
WPG were in fact 10% of TXN’s revenues, this would equate to TXN being ~14.11% of WPG’s COGS,
again using the previously established ratio13.
BSCA has now decreased the odds of randomly selecting the percentage value of the TXN-WPG
relationship by an additional 73%, from 1:37,000 to 1:10,000.
So given the result from this algorithm, Bloomberg Supply Chain analysts will continue to research on all
sources which include but are not limited to --quantified and unquantified relationships, accounting
types, financials, geographies, end markets, operating segments, products, channels, and a variety of
industry data, to determine the best possible quantified data (Bloomberg Estimate) for this relationship.
Figure 4. Estimating Range Of Relationship Possibilities (Step 3)
Source: Bloomberg
12 Some
of TXN’s distributor customers, because of their small COGS size relative to TXN’s revenues, are already
limited to a number below this SEC-derived limit. For example, for the quarter ending December 31, 2011, Fuji
Electronics (9883 JP Equity) can’t be more than ~3.09% of TXN’s revenues, because it is at this point that TXN’s
applicable semiconductor revenues would become 100% of Fuji’s applicable COGS.
13 We note that this case would be highly unlikely for two reasons. First, it assumes that we know 100% of TXN’s
distributor customers, and we rarely know 100% of any particular group. Second, it assumes that one customer,
WPG, is at the highest mathematical limit, and all of the others are at the absolute lowest limit. Such a
combination is an extraordinarily unlikely scenario.