Zyme Channel Data Validations For Accurate White Paper

About Zyme
Zyme is the leading provider of channel data solutions for global technology companies. Zyme
processes and validates millions of POS and inventory transactions each week from channel
partners in 180 countries. Zyme supports customers with verified channel sales and inventory
data for mission-critical business processes like revenue recognition, SOX compliance, incentive
payments, sales commissioning, and supply chain planning. With its unique Service-On-Software
offering, Zyme has earned an unparalleled reputation for value in the industry. Zyme provides
seamless integration of channel data with CRM and ERP systems such as Salesforce.com,
SAP and Oracle. As a recognition of its leadership, Zyme was announced a winner in the 2009
Informatica Innovation Awards, and was recognized as a 2010 Computerworld Honors Program
Laureate. For more information call 1-877-262-8993 or visit www.zyme.com.
June 2010
Channel Data Validations for Accurate Financial
Reporting and SOX Compliance
This document contains proprietary information of Zyme Solutions, Inc., based on the experience and research of Zyme and its
partners, and may not be reproduced without prior consent from Zyme Solutions, Inc. While every attempt has been made to
ensure that the information in this document is accurate and complete, some typographical errors or technical inaccuracies
may exist. The information contained in this document is subject to change without notice. Zyme does not accept responsibility
for any kind of loss resulting from the use of information contained in this document. Further, Zyme is not, by means of this
document, rendering business, financial, investment, or other professional accounting advice or services. Before making any
decision or taking any action that may affect your business, you should consult a qualified professional advisor.

2Channel Data Validations for Accurate Financial Reporting and SOX Compliance |
Table of Contents
1 Executive Summary....................................................................................... 3
2 How Channel Data is Used in Financial Reporting....................................... 3
2.1 Revenue Reporting............................................................................................................ 3
2.2 Reporting of Deferred Revenues for the Balance Sheet................................................... 4
2.3 Estimates for Accounting Allowances............................................................................... 4
2.3.1 Estimating price protection reserves...................................................................... 4
2.3.2 Estimating the Returns Allowance.......................................................................... 5
3 Validations and Analyses for Channel Parter Data........................................ 6
3.1 SISO Reconciliation........................................................................................................... 7
3.2 Inventory Aging.................................................................................................................. 9
3.2.1 Using the inventory aging report for financial reporting......................................... 9
3.2.2 Trend Analysis—Weeks of Inventory on Hand........................................................ 9
3.2.3 Using a trend analysis for financial reporting......................................................... 9
3.3 Sell-in Trend and Timing Analysis...................................................................................... 9
4 Conclusion....................................................................................................10

1 Executive Summary
The channel now accounts for 66% of overall technology sales, according to an Accenture
analysis based on Gartner data1
. Because so much revenue flows through channels, and
because Sarbanes-Oxley (SOX) has increased the industry’s scrutiny of financial data,
obtaining reliable channel partner data has become a critical component of creating accurate
financial statements. At the same time, unreliable data is a potential SOX control weakness.
Technology vendors use channel partner data to:
Calculate revenue sold through indirect channels•
Generate analyses that validate their revenue recognition decisions and accounting estimates•
Better manage the indirect channel by assessing current data against historical trends•
and ratios
Determine sales compensation•
Calculate partner incentives to be paid•
This paper discusses the role of channel partner data in financial reporting. It outlines several
data validation and analysis methods that vendors can use to ensure the reliability, accuracy,
and auditability of their financial statements and improve channel management.
2 How Channel Data Is Used in Financial Reporting
2.1 Revenue Reporting
For financial reporting purposes, the reliability and accuracy of partner-reported sales and
inventory data is important, whether a company recognizes revenue on a sell-through,
sell-in, or hybrid basis. For companies with a sell-through revenue recognition policy, in
which revenue is recognized only when product ships from distributors to resellers or end
customers, anywhere from 40% to 100% of a company’s reported revenue may be based on
partner-reported sales and inventory data.
Companies with a sell-in revenue recognition policy recognize revenue immediately when
product ships to the channel. However, future product returns, rebates, warranty claims, and
special pricing adjustments must be estimated based on historical data. Through contra-
revenue accounts, these estimates are then commonly applied as reductions to recognized
revenue on the income statement. These estimates are a prime target for scrutiny by auditors.
Partner-reported inventory data provides the required audit trail to support the adequacy of
the various allowances and reserves for special pricing, returns, warranty, and incentives.
In calculating revenue to be recognized, companies must assign prices to units sold
through the channel. This can be a complicated task for companies that use the sell-through
approach for any portion of their indirect sales. This task can be accomplished through a
number of methods:
On a first-in, first-out (FIFO) basis, which assumes that the units sold into the channel first•
were also sold out of the channel first. To account for price protection or other special
pricing situations, the applicable price for revenue recognition purposes is the price listed
on the date of sell-through. Exceptions must be made when the listed price is greater
than the sell in invoice price, when the sold-through goods are no longer eligible for
recent price protections, etc. Because of the complexity of the rules for pricing transac-
tions, it is especially critical for audit purposes for companies to have a reliable, consis-
tent process, as well as “clean” and accurate channel data to work with.
On a serial number basis, in which the serial numbers of point of sale (POS) units or the•
units remaining in inventory are tied to their respective sell-in invoices, thereby using the
exact sell-in price of the unit for accounting.

On the basis of an assigned value based on price books.•
On the basis of an assigned value based on a calculated average partner sell-in price.•
As the flip side of the revenue calculation, the FIFO or serial-number methods are also applied
to channel inventory to generate an inventory aging report, which details the number of units
held in inventory for given time frames and the current value of those units. The inventory
aging report can be used to adjust allowances for returns and warranty claims, as well as to
evaluate the overall health of the channel.
2.2 Reporting of Deferred Revenues for the Balance Sheet
To recognize revenue for some portion of their indirect sales, many companies create a
“deferred revenues” entry on the balance sheet for units that have been sold into the channel
but have not yet been sold through. This is calculated by subtracting total partner-reported
sell-through from total sell-in. For products with relatively stable prices, the price per unit is
determined using an average selling price (ASP). For products with more volatile pricing, the
price can be established by associating the partner-reported unsold inventory to the sell-in
invoices on a FIFO basis, using adjustments for recent pricing actions.
2.3 Estimates for Accounting Allowances
As discussed above, it is common to use channel partner POS and inventory data to estimate
the reserves required for revenue adjustments due to price protection, tier 2 incentives, warranty
claims, returns, and inventory obsolescence. Below are some examples of this process.
2.3.1 Estimating price protection reserves
Many high-tech companies use price protection to ensure an adequate level of distributor
inventory in the channel. However, by definition, price protection means that the sale price is
not final at the time of sell-in. Thus, making accurate projections of price protection liability
and allowing for this liability becomes important.
In general, companies govern price protection through a number of rules. For example, it is
typical for only recent inventory (e.g., inventory procured within the last two months) to be price
protected. To estimate the price protection allowance, companies generally look at past sell-
through rates to determine the quantity of goods that would be in the channel within a specified
period of time after sell-in. A more accurate method is the FIFO method. FIFO is based on
actual data, while looking at past sell-through rates provides a more general estimate.
Reports such as the example report below would help the company’s finance manager assess
the adequacy of allowances made. This report shows, by SKU and distributor, the estimated
value of price protection claims, corresponding to inventory of various ages, that result from a
10% decrease in price. Vendors can look at the liability by inventory age and adjust the timing
of their price changes in order to minimize price protection claims.
Figure 1: Report of Liability for Price Protection Claims; by SKU, Distributor, and Age of Inventory Units
Partner SKU
Liability Resulting from a 10% Decrease in Price (by Inventory Age)
1 Week
or less
1 to
Weeks
2 to 4
Weeks
4 to 8
Weeks
8 to 12
Weeks
More
than 12
Weeks
Total
Distributor A
SKU-A $100 $200 $100 $100 $0 $0 $500
SKU-B $0 $0 $0 $500 $400 $500 $1,400
SKU-C $23 $0 $10 $0 $0 $0 $33
Sub total $123 $200 $110 $600 $400 $500 $1,933
Distributor B
SKU-A $0 $100 $20 $100 $0 $0 $220
SKU-B $0 $0 $0 $0 $600 $0 $600
SKU-C $50 $30 $10 $0 $0 $0 $90
Sub total $50 $130 $30 $100 $600 $0 $910
Total $175 $330 $140 $700 $1,100 $500 $2,843

2.3.2 Estimating the Returns Allowance
Most companies provide an allowance for future returns. Typically, this allowance is estimated
based on the quantity of return material authorizations (RMAs) received historically in
proportion to sales. However, additional checks are necessary under some circumstances,
such as the launch of a new product with no comparable history, changes in the overall
business environment, or the high variability of past return rates.
Greater-than-average return rates can be discovered by comparing the number of recent
POS returns and RMAs with the historical average. If the POS return rate has been steadily
increasing or decreasing in recent months, the current return allowance might have to be
revised. A report like the one shown in the example below, which compares the POS returns
to sell-through during various periods over the prior year, would help the finance manager
determine the adequacy of the return reserves. In this example, return reserves are currently
set at 3.5% of sell through. SKU’s with return rates between 3.5% and 3.99% are flagged
in light gray, while those over 4% are flagged in dark gray. Managers can easily identify
return rates that exceed current allowances over a period of time and adjust the allowances
accordingly. Using the graph, managers can spot trends over time by distributor and use the
information to better manage their channel partners. Similar reports could also be prepared
based on RMA and sell-in data.
Figure 2: Returns as a Percent of Sell-Through (Rates Exceeding Currently Established Reserve Levels are Flagged)
Partner SKU
12-
Month
Return
Rate
6-
Month
Return
Rate
3-
Month
Return
Rate
1-
Month
Return
Rate
12-
Month
Sell-
Through
12-
Month
POS
Return
6-
Month
Sell-
Through
6-
Month
POS
Return
3-
Month
Sell
Through
3-
Month
POS
Return
1-
Month
Sell
through
1-
Month
POS
Return
Dist A
SKU-A 3.00% 3.4% 3.10% 3.20% $57,828 $1,735 $29,212 $993 $15,005 $465 $4,567 $146
SKU-B 3.60% 4.00% 4.80% 5.60% $65,122 $2,344 $29,034 $1,161 $11,892 $571 $3,412 $191
SKU-C 2.9% 2.8% 3.20% 3.00% $97,612 $2,831 $53,312 $1,493 $27,098 $867 $10,966 $329
Sub total 3.13% 3.27% 3.52% 3.52% $220,562 $6,910 $111,558 $3,647 $53,995 $1,903 $18,945 $666
Dist B
SKU-A 3.20% 2.90% 3.10% 3.10% $23,123 $740 $11,560 $335 $5,991 $186 $1,808 $56
SKU-B 3.45% 3.98% 4.40% 5.10% $62,422 $2,154 $31,211 $1,242 $13,122 $577 $4,800 $245
SKU-C 2.88% 2.88% 3.00% 2.87% $10,999 $317 $5,989 $172 $3,400 $102 $900 $26
Sub total 3.33% 3.59% 3.84% 4.35% $96,544 $3,210 $48,760 $1,750 $22,513 $865 $7,508 $327
Total 3.19% 3.37% 3.62% 3.75% $317,106 $10,120 $160,318 $5,397 $76,508 $2,768 $26,453 $ 993
Figure 3: Overall Return Rate and Return Rate by Distributor for Given Time Periods

When, for a period of several weeks, there are high levels of inventory for a product
whose sales are generally slowing, this serves as another leading indicator that higher-
than-normal returns can be expected. The example report below highlights a case where
abnormally high inventory is present for products that are not selling well (i.e., aged
inventory). This report, in conjunction with the one that analyzes return rates, would
help the decision-maker zero in on cases where the return allowances might need to be
adjusted from normal levels. Such reports could prove useful in cases such as a new
product launch where its market acceptance is different from that of similar products. For
example, there are currently 14 weeks of inventory of SKU-B at Distributor A, and 64% of
that inventory is older than 8 weeks. Distributor B is also reporting a buildup of SKU-B.
inventory over 8 weeks. This is a flag to look more closely at the return reserves for SKU-B.
3 Validations and Analyses for Channel Partner Data
Accurate channel partner data is a critical input for reliable financial statements. However,
most companies have little ability to assess the data from their partners—they must accept
it on the “honor system.” To ensure that the data is complete and accurate enough to
be used for financial reporting, there are several levels of validation and analysis that are
considered best practices.
Figure 4: Inventory Aging Report, by Distributor and SKU
Partner SKU
1
Week
or
Less
1 to 2
Weeks
2 to 4
Weeks
4 to 8
Weeks
8 to 12
Weeks
Over
12
Weeks
Total
Unsold
Inventory
(Units)
Total
Unsold
Inventory
($)
Inventory
Older
Than
8 Weeks
Weeks of
Inventory
Dist A
SKU-A $1,000 $2,000 $1,000 $1,000 $0 $0 125 $5,000 0% 5
SKU-B $0 $0 $0 $5,000 $4,000 $5,000 700 $14,000 64% 14
SKU-C $225 $0 $100 $0 $0 $0 13 $325 0% 0.7
Sub total $1,225 $2,000 $1,100 $6,000 $4,000 $5,000 838 $19,325 47% 7.7
Dist B
SKU-A $0 $1,000 $200 $1,000 $0 $0 55 $2,200 0% 5
SKU-B $0 $0 $0 $0 $6,000 $0 300 $6,000 100% 5
SKU-C $500 $300 $100 $0 $0 $0 36 $900 0% 4
Sub total $500 $1,300 $300 $1,000 $6,000 $0 391 $9,100 66% 4.9
Total $1,725 $3,300 $1,400 $7,000 $10,000 $5,000 1,229 $28,425 53% 6.5
Figure 5: Graph of Inventory Balances, by Partner and Age of Inventory

Sales in, sales out (SISO) reconciliation is a good first step to take in establishing the
reliability and accuracy of the data used in financial statements. Additional analyses serve
to highlight and resolve such business issues, as well as to improve the accuracy of certain
accounting allowances. For example, to determine whether reported channel partner
inventory levels are reasonable and accurate, finance personnel can analyze the trend of
weeks of inventory on hand to see whether it is increasing. If the inventory is increasing,
finance personnel might use an inventory aging report to compare its data against trends
in the SISO ratio and observe historical seasonality. Over an extended period of time, sales
into the channel and sales out should match closely; however, inventory may increase
seasonally, as it does prior to the U.S. holiday season. A report that shows the ratio of
sell-in to sell-out over a rolling period of between 4 and 8 weeks can highlight cases
where sell-in and sell-out are not aligned. The aging report can provide important insights
about whether the inventory is aged or has built up recently, in order to support expected
future spikes in demand. A more detailed discussion of some useful channel management
analysis tools follows.
3.1 SISO Reconciliation
The SISO reconciliation approach can serve as an internal SOX control (Section 404)
that can give a manufacturer confidence in distributor-reported data. In this method, the
sell-through and inventory data provided by the channel partner is compared to the sell-in
data to see whether variances between them fall within reasonable limits that could be
explained by causes such as transit time. The method relies on making an estimate of the
inventory (i.e., calculated inventory) based on the previous week’s calculated inventory,
SISO data, etc. The calculated inventory for SISO reconciliation is computed as follows:
Calculated inventory =
Previous week’s calculated inventory
+ Sell-in
+ Transfers from other partners
– Transfers to other partners
– Sell-through
+ Returns to partner
– RMAs (returns to the company)
– Adjustments (scrap, theft, etc.)
The difference between calculated inventory and partner-reported inventory is the
variance, or delta. The sign of the delta can be positive or negative—variance in either
direction is equally relevant—so the absolute value of the delta for each SKU and channel
partner is considered. Variances are expressed as a fraction of the partner-reported
inventory. An acceptable variance depends on a number of factors, including product type,
volume of sales for the product, and region. The primary output of SISO reconciliation is a
report, such as the one below, that gives a quick snapshot of which partners have reliable
data and which do not. The first four partners on the list have a delta equaling or exceeding
a predefined limit of 20%, while the next three are approaching the limit.

The trend of the deltas is also of interest. If the calculated inventory diverges continuously
(i.e., week after week in either direction) from the reported inventory, this is a cause for
concern and should be investigated. The calculated and reported inventories are not
expected to match in most weeks, but a close convergence between the two is expected.
SISO reconciliation can highlight several types of issues:
Inaccurate POS and inventory data•
Errors or inadequacies in the internal process for aggregating and validating channel•
partner data
Operational issues, such as the failure to capture data from certain SKUs or warehouses•
Underlying market dynamics, such as a buildup of channel inventory due to changing•
demand patterns
Once an issue has been flagged due to a SISO reconciliation variance, additional
validations can be performed to identify the root cause of the variance and make
adjustments by:
Working with channel partners to improve the quality and timeliness of their data through•
partner reporting requirements and compliance scorecards
Identifying and resolving errors in the aggregation and validation of data•
Resolving basic operational issues, such as the failure to capture data from certain ware-•
houses or SKUs
Identifying inventory buildups or shortages, inaccurate claims payments, etc.•
Figure 6: SISO Reconciliation Report Highlighting Partners with an Unacceptable Delta Between Reported and
Calculated Inventory
Partner
Abs
Delta
($)
Abs Delta
(Units)
Net Delta
($)
Net
Delta
(Units)
MR
Rpt Inv
($)
Shipments
($)
End
Calc Inv
($)
Abs %
Delta
($)
Arlow USA 1,522,452 6,103,880 (1,499,362) (5,978,770) - 3,335,233 1,499,362 -
Gomee 1,142,993 1,199,583 (1,027,411) (283,002) 2,857,109 64,954,341 3,884,520 40%
Kago Electronics 3,557,869 4,771,169 (242,524) (1,281,372) 14,792,307 201,477,790 15,034,832 24%
Ingra USA 1,363,817 2,057,873 325,817 447,771 6,794,734 181,578,399 6,468,917 20%
Digita China 726,254 1,255,981 (425,525) (722,703) 3,813,553 73,188,378 4,239,077 19%
Futuro Europe 1,292,027 4,487,947 (1,208,770) (4,329,908) 7,274,943 88,769,815 8,483,713 18%
BCI Camera 299,229 1,046,747 (144,499) (782,838) 1,875,064 31,137,020 2,019,564 16%
Intcomix 1,046,137 3,560,762 568,138 1,842,213 7,302,110 76,808,770 6,733,972 14%
Soga Computers 5,363,695 22,508,005 (722,785) 5,230,947 46,284,456 497,356,692 47,007,240 12%
Futuro AsiaPac 1,004,887 7,310,095 989,740 7,106,040 8,910,507 69,179,734 7,920,767 11%
Alabella 612,285 1,670,309 (420,119) (1,084,744) 6,531,493 94,721,739 6,951,613 9%
Officeworkers 5,379,998 22,927,356 1,930,000 3,646,953 60,758,752 789,228,521 58,828,752 9%
Ingra Canada 1,463,143 5,212,184 204,483 (129,185) 17,979,541 207,628,324 17,775,057 8%
Havnet Europe 690,694 2,582,110 (502,731) (1,987,277) 10,736,050 172,847,900 11,238,781 6%
Best Buyers USA 1,057,344 5,564,791 (880,108) (5,324,152) 18,349,209 134,595,238 19,229,318 6%
Total 30,790,751 105,843,998 (2,516,631) 425,816 253,164,030 3,021,900,338 255,680,661 12%

3.2 Inventory Aging
A report on the age of inventory held by the partner can be used as a tool to improve
overall channel management—for example, aging inventory can highlight unintentional
channel stuffing. Two common methods for assessing inventory aging include:
FIFO method. In this method, the age of the inventory is estimated by assuming that•
the goods that were sold into the channel first were also sold through the channel first.
Partner-reported inventory units are therefore assigned to the latest sell-in invoices, and
the dates of these invoices provide an estimate of the age of the inventory. It’s also pos-
sible to calculate an average inventory age weighted by value.
Serial number method. This method is more accurate, but it requires partners to provide•
serial numbers for each individual unit in inventory. The serial numbers can be tied back
to the sell-in data for each unit, and the age of each unit can be estimated based on the
date it was shipped from the company.
3.2.1 Using the inventory aging report for financial reporting
In addition to its use as a channel management tool, the inventory aging report is required for:
Estimating reserves for inventory obsolescence—as inventory ages, it is more likely to•
become obsolete
Estimating price protection reserves—only inventory that is older than a given threshold•
(e.g., older than two months) is eligible for price protection
Assessing the adequacy of warranty/return reserves in special circumstances, such as:•
o New products that are not comparable to those for which historical return data exists
o Changes in the overall business environment
o Large variations in return rates in the past
In these cases, observing very high weeks of inventory of a product whose sales are
slowing down overall indicates that higher-than-normal returns could be expected.
3.2.2 Trend Analysis—Weeks of Inventory on Hand
Weeks of inventory on hand is calculated by dividing on-hand inventory by a four-week
average of weekly sales. Typically, this metric is used in conjunction with the inventory
aging report. For channel management purposes, an increase in the weeks of inventory on
hand may highlight issues such as slowing demand or channel stuffing.
3.2.3 Using a trend analysis for financial reporting
As a quick method of assessing excess inventory in the channel, the weeks of inventory on
hand metric is often used to help make the appropriate adjustments to various allowances.
When the value of this metric is very high, it could indicate additional liability for any
upcoming pricing action (due to price protection), and if the value is high primarily due to
aged inventory, this could indicate a higher possibility of returns.
3.3 Sell-in Trend and Timing Analysis
In the absence of upcoming product promotions, sudden spikes in the quantity of sell-
in inventory could be a cause for concern. An analysis of the weekly sell-in quantity in
conjunction with the weeks of inventory on hand could highlight instances of unnecessary
sales into the channel.
In general, over an extended period of time, the sell-in and sell-out quantities should match
closely. A report showing the ratio of sell-in to sell-out over a rolling period between 4 and
13 weeks would help highlight cases where the sell-in is not aligned to the sell-out.

4 Conclusion
Inaccurate channel partner data or an inadequate process for consolidating and
reconciling channel partner data can lead to material financial statement issues, such as:
Inaccurate revenue reporting•
Inaccurate balance sheet numbers (deferred revenues)•
Inaccurate accounting estimates or an inadequate audit trail for accounting estimates,•
such as:
o Reserves for returns and warranty claims
o Reserves for rebates, price protection, and other special pricing programs
o Allowances for inventory obsolescence
These problems arise in part due to the nature of the financial reporting process. Instead
of setting specific, detailed rules for internal controls around the financial reporting
process, SOX-compliant financial reporting requires accounting and finance departments
to make many judgment calls, guided by the high-level principles set forth by the Public
Company Accounting Oversight Board (PCAOB) and the SEC.
With the onus on companies to provide adequate supporting information for their financial
reporting decisions and to demonstrate that they have proper controls over financial data
in place, obtaining timely and accurate POS and inventory data from channel partners
becomes a critical step in the financial reporting process. By focusing on validations and
analysis of this data, companies can improve the reliability, accuracy, and auditability of
their financial statements and can better manage their indirect channel.
1
“Developing Indirect Channels: A Structured Approach to Reaching New Customers and Growing Revenues.”
Accenture, May 27, 2009.

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